Relevant bibliographies by topics / RMSE

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'RMSE'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 September 2023

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Journal articles on the topic "RMSE"

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Chisanga, Charles B., Elijah Phiri, and Vernon R. N. Chinene. "Evaluating APSIM-and-DSSAT-CERES-Maize Models under Rainfed Conditions Using Zambian Rainfed Maize Cultivars." Nitrogen 2, no. 4 (September 23, 2021): 392–414. http://dx.doi.org/10.3390/nitrogen2040027.

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Crop model calibration and validation is vital for establishing their credibility and ability in simulating crop growth and yield. A split–split plot design field experiment was carried out with sowing dates (SD1, SD2 and SD3); maize cultivars (ZMS606, PHB30G19 and PHB30B50) and nitrogen fertilizer rates (N1, N2 and N3) as the main plot, subplot and sub-subplot with three replicates, respectively. The experiment was carried out at Mount Makulu Central Research Station, Chilanga, Zambia in the 2016/2017 season. The study objective was to calibrate and validate APSIM-Maize and DSSAT-CERES-Maize models in simulating phenology, mLAI, soil water content, aboveground biomass and grain yield under rainfed and irrigated conditions. Days after planting to anthesis (APSIM-Maize, anthesis (DAP) RMSE = 1.91 days; DSSAT-CERES-Maize, anthesis (DAP) RMSE = 2.89 days) and maturity (APSIM-Maize, maturity (DAP) RMSE = 3.35 days; DSSAT-CERES-Maize, maturity (DAP) RMSE = 3.13 days) were adequately simulated, with RMSEn being <5%. The grain yield RMSE was 1.38 t ha−1 (APSIM-Maize) and 0.84 t ha−1 (DSSAT-CERES-Maize). The APSIM- and-DSSAT-CERES-Maize models accurately simulated the grain yield, grain number m−2, soil water content (soil layers 1–8, RMSEn ≤ 20%), biomass and grain yield, with RMSEn ≤ 30% under rainfed condition. Model validation showed acceptable performances under the irrigated condition. The models can be used in identifying management options provided climate and soil physiochemical properties are available.
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EAJAZ AHMAD DAR, GERRIT HOOGENBOOM, and ZAHOOR AHMAD SHAH. "Meta analysis on the evaluation and application of DSSAT in South Asia and China: Recent studies and the way forward." Journal of Agrometeorology 25, no. 2 (May 25, 2023): 185–204. http://dx.doi.org/10.54386/jam.v25i2.2081.

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The Decision Support System for Agrotechnology transfer (DSSAT) is a global modelling platform that encompasses crop models for more than 40 different crops. The models have been used extensively throughout the world, including South Asia and China. From the web of science database, we reviewed 205 papers that were published from January 2010 to February 2022 containing examples of the evaluation and application of the DSSAT crop simulation models. In South Asia and China, more than 50 traits and variables were analyzed for various experiments and environmental conditions during this period. The performance of the models was evaluated by comparing the simulated data with the observed data through different statistical parameters. Over the years and across different locations, the DSSAT crop models simulated phenology, growth, yield, and input efficiencies reasonably well with a high coefficient of determination (R2), and Willmott d-index, together with a low root mean square error (RMSE), normalized RMSE (RMSEn), mean error (ME) or percentage error difference. The CERES models for rice, wheat and maize were the most used models, followed by the CROPGRO models for cotton and soybean. Grain yield, anthesis and maturity dates, above ground biomass, and leaf area index were the variables that were evaluated most frequently for the different crop models. The meta-analysis of the data of the most common simulated variables (Anthesis, maturity, leaf area index, grain yield and above ground biomass) for the four commonly used DSSAT models (CERES-Rice, CERES-Wheat, CERES-Maize and CROPGRO-Cotton) showed that the models predicted anthesis with an RMSE of ~2 (CERES-Maize) and -4 days (CERES-Wheat), a normalized RMSE of ~2.5 (CERES-Maize) and -3.8% (CERES-Rice), and a R2 ~ 0.98-0.99. The maturity was predicted with an RMSE~ 3.0 (CERES-Maize)-6.1 days (CROPGRO-Cotton), normalized RMSE~2.3 (CERES-Wheat)-5.0% (CERES-Rice) and R2 ~ 0.90-0.99. The leaf area index was predicted with an RMSE~ 0.3-0.7, normalized RMSE~6 (CROPGRO-Cotton)-16% (CERES-Maize) and R2 ~ 0.75-0.98. The model performance for simulating grain yield was best with CROPGRO-cotton with a normalized RMSE of 4.4%, RMSE of 138.8 kg and R2 of 0.99. The lowest R2 and highest RMSEn was found for CERES-Wheat. Among all the variables that were evaluated, above ground biomass was least accurately simulated with a RMSEn as high as 18% and R2 as small as 0.50 by CERES-Wheat. The models were used for studying the crop response under various soil, weather, and management conditions. The review will be helpful to identify the research gap in the use of crop models for different crops in South Asia and China. It can also aid scientists to target their research for specific applications to address food and nutrition security based on sustainable management practices.
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Chai, T., and R. R. Draxler. "Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature." Geoscientific Model Development 7, no. 3 (June 30, 2014): 1247–50. http://dx.doi.org/10.5194/gmd-7-1247-2014.

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Abstract. Both the root mean square error (RMSE) and the mean absolute error (MAE) are regularly employed in model evaluation studies. Willmott and Matsuura (2005) have suggested that the RMSE is not a good indicator of average model performance and might be a misleading indicator of average error, and thus the MAE would be a better metric for that purpose. While some concerns over using RMSE raised by Willmott and Matsuura (2005) and Willmott et al. (2009) are valid, the proposed avoidance of RMSE in favor of MAE is not the solution. Citing the aforementioned papers, many researchers chose MAE over RMSE to present their model evaluation statistics when presenting or adding the RMSE measures could be more beneficial. In this technical note, we demonstrate that the RMSE is not ambiguous in its meaning, contrary to what was claimed by Willmott et al. (2009). The RMSE is more appropriate to represent model performance than the MAE when the error distribution is expected to be Gaussian. In addition, we show that the RMSE satisfies the triangle inequality requirement for a distance metric, whereas Willmott et al. (2009) indicated that the sums-of-squares-based statistics do not satisfy this rule. In the end, we discussed some circumstances where using the RMSE will be more beneficial. However, we do not contend that the RMSE is superior over the MAE. Instead, a combination of metrics, including but certainly not limited to RMSEs and MAEs, are often required to assess model performance.
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Sezen, Semih Metin, Ishfaq Ahmad, Muhammad Habib-ur-Rahman, Ebrahim Amiri, Servet Tekin, Kadir Can Oz, and Clever Mwika Maambo. "Growth and productivity assessments of peanut under different irrigation water management practices using CSM-CROPGRO-Peanut model in Eastern Mediterranean of Turkey." Environmental Science and Pollution Research 29, no. 18 (December 3, 2021): 26936–49. http://dx.doi.org/10.1007/s11356-021-17722-w.

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AbstractIrrigation water scheduling is crucial to make the most efficient use of ever-decreasing water. As excessive irrigation decreases yield, while imprecise application also causes various environmental issues. Therefore, efficient management of irrigation frequency and irrigation level is necessary to sustain productivity under limited water conditions. The objective of the current study is to assess the water productivity at various irrigation regimes during peanut crop growing seasons (2014 and 2015) in Eastern Mediterranean, Turkey. The field experiments were conducted with treatments consisting of three irrigation frequencies (IF) (IF1: 25 mm; IF2: 50 mm; and IF3: 75 mm of cumulative pan evaporation (CPE)), and four irrigation water levels (WL1 = 0.50, WL2 = 0.75, WL3 = 1.0, and WL4 = 1.25). WL1, WL2, WL3, and WL4 treatments received 50, 75, 100, and 125 of cumulative pan evaporation. The CSM-CROPGRO-Peanut model was calibrated with experimental data in 2014 and evaluated with second-year experimental data (2015). The model simulated seed yield and final biomass (dry matter) reasonably well with low normalized root mean square error (RMSEn) in various irrigation intervals. The model simulated reasonably well for days to anthesis (RMSE = 2.53, d-stat = 0.96, and r2 = 0.90), days to physiological maturity (RMSE = 2.55), seed yield (RMSE = 1504), and tops biomass dry weight at maturity (RMSE = 3716). Simulation results indicated good agreement between measured and simulated soil water content (SWC) with low RMSEn values (4.0 to 16.8% in 2014 and 4.3 to 18.2% in 2015). Further results showed that IF2I125 irrigation regime produced the highest seed yield. Generally, model evaluation performed reasonably well for all studied parameters with both years’ experimental data. Results also showed that the crop model would be a precision agriculture tool for the extrapolation of the allocation of irrigation water resources and decision management under current and future climate.
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Cunha, Nicolas Cabral, Arissa Ikeda Suzuki, Fernanda Ferreira da Silva Lima, Priscila Valverde Fernandez, Paulo Antônio Silvestre de Faria, Teresa de Souza Fernandez, and Sima Esther Ferman. "Alterações Citogenético-Moleculares no Gene FOXO1 em uma Criança com Rabdomiossarcoma Alveolar: Relato de Caso." Revista Brasileira de Cancerologia 64, no. 3 (February 15, 2019): 415–19. http://dx.doi.org/10.32635/2176-9745.rbc.2018v64n3.51.

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Introdução: O rabdomiossarcoma (RMS) é o tumor de tecidos moles mais comum da infância. Pode ser classificado em dois subtipos principais: o rabdomiossarcoma alveolar (RMSa) e o embrionário (RMSe). No RMSa, o prognóstico é desfavorável quando comparado ao RMSe, necessitando de tratamento intensificado; dessa forma, a distinção entre ambos os subtipos é fundamental. Citogeneticamente, o RMSa apresenta translocações cromossômicas envolvendo o gene FOXO1 em 80% dos casos. A metodologia de hibridização in situ por fluorescência (FISH) tem sido muito utilizada para caracterizar o RMSa. Relato do caso: Paciente do sexo feminino, com 7 anos de idade, apresentou ao diagnóstico RMSa parameníngeo, sem metástase ao diagnóstico. A análise por meio de FISH mostrou a translocação envolvendo o gene FOXO1 e uma cópia extra desse gene. A paciente foi incluída no protocolo de tratamento do EpSSG, classificada como grupo de alto risco e recebeu quimioterapia e radioterapia. No final do tratamento, foi observada resposta parcial e iniciada quimioterapia de segunda linha. Não houve resposta clinicorradiológica e a paciente evoluiu com progressão de doença local refratária ao tratamento e óbito após um ano do diagnóstico. Conclusão: De acordo com o nosso conhecimento, é a primeira descrição de um caso de RMSa apresentando a translocação do gene FOXO1 e uma cópia extra desse gene em clones separados. São necessários ainda novos estudos, a fim de compreender melhor o significado prognóstico da presença dessas alterações.
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Mentaschi, L., G. Besio, F. Cassola, and A. Mazzino. "Problems in RMSE-based wave model validations." Ocean Modelling 72 (December 2013): 53–58. http://dx.doi.org/10.1016/j.ocemod.2013.08.003.

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Chai, T., and R. R. Draxler. "Root mean square error (RMSE) or mean absolute error (MAE)?" Geoscientific Model Development Discussions 7, no. 1 (February 28, 2014): 1525–34. http://dx.doi.org/10.5194/gmdd-7-1525-2014.

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Abstract. Both the root mean square error (RMSE) and the mean absolute error (MAE) are regularly employed in model evaluation studies. Willmott and Matsuura (2005) have suggested that the RMSE is not a good indicator of average model performance and might be a misleading indicator of average error and thus the MAE would be a better metric for that purpose. Their paper has been widely cited and may have influenced many researchers in choosing MAE when presenting their model evaluation statistics. However, we contend that the proposed avoidance of RMSE and the use of MAE is not the solution to the problem. In this technical note, we demonstrate that the RMSE is not ambiguous in its meaning, contrary to what was claimed by Willmott et al. (2009). The RMSE is more appropriate to represent model performance than the MAE when the error distribution is expected to be Gaussian. In addition, we show that the RMSE satisfies the triangle inequality requirement for a distance metric.
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Scotto, Carlo, and Dario Sabbagh. "The Accuracy of Real-Time hmF2 Estimation from Ionosondes." Remote Sensing 12, no. 17 (August 19, 2020): 2671. http://dx.doi.org/10.3390/rs12172671.

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A total of 4991 ionograms recorded from April 1997 to December 2017 by the Millstone Hill Digisonde (42.6°N, 288.5°E) were considered, with simultaneous Ne(h)[ISR] profiles recorded by the co-located Incoherent Scatter Radar (ISR). The entire ionogram dataset was scaled with both the Autoscala and ARTIST programs. The reliability of the hmF2 values obtained by ARTIST and Autoscala was assessed using the corresponding ISR values as a reference. Average errors Δ and the root mean square errors RMSE were computed for the whole dataset. Data analysis shows that both the Autoscala and ARTIST systems tend to underestimate hmF2 values with |Δ| in all cases less than 10 km. For high magnetic activity ARTIST offers better accuracy than Autoscala, as evidenced by RMSE[ARTIST] < RMSE[Autoscala], under both daytime and nighttime conditions, and considering all hours of the day. Conversely, under low and medium magnetic activity Autoscala tends to estimate hmF2 more accurately than the ARTIST system for both daytime and nighttime conditions, when RMSE[Autoscala] < RMSE[ARTIST]. However, RMSE[Autoscala] slightly exceeds RMSE[ARTIST] for the day as a whole. RMSE values are generally substantial (RMSE > 16 km in all cases), which places a limit on the results obtainable with real-time models that ingest ionosonde data.
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Hastomo, Widi, Adhitio Satyo Bayangkari Karno, Nawang Kalbuana, Ervina Nisfiani, and Lussiana ETP. "Optimasi Deep Learning untuk Prediksi Saham di Masa Pandemi Covid-19." Jurnal Edukasi dan Penelitian Informatika (JEPIN) 7, no. 2 (August 11, 2021): 133. http://dx.doi.org/10.26418/jp.v7i2.47411.

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Penelitian ini bertujuan untuk meningkatkan akurasi dengan menurunkan tingkat kesalahan prediksi dari 5 data saham blue chip di Indonesia. Dengan cara mengkombinasikan desain 4 hidden layer neural nework menggunakan Long Short Term Memory (LSTM) dan Gated Recurrent Unit (GRU). Dari tiap data saham akan dihasilkan grafik rmse-epoch yang dapat menunjukan kombinasi layer dengan akurasi terbaik, sebagai berikut; (a) BBCA dengan layer LSTM-GRU-LSTM-GRU (RMSE=1120,651, e=15), (b) BBRI dengan layer LSTM-GRU-LSTM-GRU (RMSE =110,331, e=25), (c) INDF dengan layer GRU-GRU-GRU-GRU (RMSE =156,297, e=35 ), (d) ASII dengan layer GRU-GRU-GRU-GRU (RMSE =134,551, e=20 ), (e) TLKM dengan layer GRU-LSTM-GRU-LSTM (RMSE =71,658, e=35 ). Tantangan dalam mengolah data Deep Learning (DL) adalah menentukan nilai parameter epoch untuk menghasilkan prediksi akurasi yang tinggi.
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Mokhtar, N. M., N. Darwin, M. F. M. Ariff, Z. Majid, and K. M. Idris. "THE CAPABILITIES OF UNMANNED AERIAL VEHICLE FOR SLOPE CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W16 (October 1, 2019): 451–59. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w16-451-2019.

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Abstract. Slope classification mapping is an important component of land suitability analysis for preventing landslides. This study aim to investigate the capabilities and application of Unmanned Aerial Vehicle (UAV) platform for slope classification. The objectives of this study such as investigating the capabilities of UAV for slope classification, generating Digital Elevation Model (DEM) and orthophoto from the image acquired and assessing the accuracy of DEM and orthophoto produced for slope classification. In this study, the aerial image was acquired using UAV at 60 m and 40 m altitude will then generates the DEM and orthophoto used to produce the slope map and classify the slope. The UAV data was validated with the check points observed from ground survey using GPS to obtain the Root Mean Square Error (RMSE) values. The RMSE value for UAV derived DEM at 60 m altitude is ±0.234 m and ±0.604 m for X and Y respectively. The average RMSE is ±0.279 m. The average RMSE value obtained from LiDAR derived DEM in previous research is ±0.616 m. The RMSE value for UAV derived DEM at 40 m altitude is ±0.596 m and ±0.405 for X and Y respectively. The average RMSE is ±0.334 m. The average RMSE value obtained from LiDAR derived DEM in previous research is ±0.450 m. In conclusion, it shows that the RMSE value obtained from UAV derived DEM is smaller than the RMSE value obtained from LiDAR derived DEM. Hence, UAV is capable for the generation of slope map and slope classification.
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Dissertations / Theses on the topic "RMSE"

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SAH, BIKASH KUMAR. "A NOVEL CONVOLUTIONAL NEURAL NETWORK FOR AIR POLLUTION FORECASTING." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18792.

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Air pollution was a global problem a few decades back. It is still a problem and will continue to be a problem if not solved appropriately.Various machine learning and deep learining approaches have been purposed for accurate prediction, estimation and analysis of the air polution. We have purposed a novel five layer one-dimensional convolution neural network architecture to forecast the PM2.5 concentration. It is a deep learning approach. We have used the five year air pollution dataset from 2010 to 2014 recorded by the US embassy in Beijing, China taken from the database from UCI machine learining repository [19]. The dataset we are considering is in the .csv format. The dataset consists of feature columns like “Number,” “year,” “month,” “day,” “PM2.5”, “PM10”, “S02”, “dew,” “temp,” “pressure,” “wind direction,” “wind direction,” “snow” and “rain.” The dataset consisted of a total of 43,324 rows and nine feature columns.The model yields the best results in predicting PM2.5 levels with an RMSE of 28.1309 and MAE of 14.9727. On statistical analysis we found that ur proposed prediction model outperformed the traditional forecasting models like DTR, SVR and ANN models for the air pollution forecasting.
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Chermiti, Amro. "Hur kan injicerad aktivitet individanpassas vid skelettscintigrafi? Effekten av patientspecifika parametrar." Thesis, Örebro universitet, Institutionen för hälsovetenskaper, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-84602.

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Bakgrund: Skelettscintigrafi är en nuklearmedicinsk undersökning. Undersökningen är den mest använda nukleardiagnostiska metoden och den genomförs ofta som en helkroppsundersökning. För att undersökningen ska kunna erhålla sin diagnostiska kvalitet, samt följa strålsäkerhetsmyndighetens rekommendationer behövs det mer kännedom till hur optimeringen ska följa as low as reasonably achievable (ALARA). Studiens syfte var att optimera patientstråldos samt att undersöka hur injicerad aktivitet kan anpassas efter patientens specifika parametrar. Metod: Studiegruppen bestod av 85 patienter som genomgick skelettscintigrafier vid Central sjukhuset i Karlstad, från perioden februari-april 2020. Resultat: Visade att både ålder och vikt är patientspecifika variabler som borde tas till betraktning vid bestämning av injicerad strålningsdos. Konklusionen: För att optimera undersökningen för varje patient bör injicerad aktivitet anpassas efter både kroppsvikt och ålder. Fler studier där andra parametrar undersöks måste genomföras.
Background: Bone scintigraphy is a nuclear medicine procedure. It is the most used nuclear diagnostic method and provides the opportunity to perform a full-body examination. For the method to retain its diagnostic quality, and to follow the recommendations of the Radiation Safety Authority, more knowledge is required on how the optimization should follow as low as reasonably achievable (ALARA). The purpose of the study was to optimize patient radiation dose and to investigate how the injected activity can be adapted to patient-specific parameters. Method: The study group consisted of 85 patients who underwent bone scintigraphy at the Central Hospital in Karlstad, from the period February-April 2020. Result: Showed that age and weight are patient-specific variables that should be considered when determining injected radiation dose. Conclusion: To optimize the examination for each patient, injected activity should be adjusted according to the patient’s body weight and age. More studies in where other parameters are investigated must be carried out.
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Hast, Isak. "Quality Assessment of Spatial Data: Positional Uncertainties of the National Shoreline Data of Sweden." Thesis, Högskolan i Gävle, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-18743.

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This study investigates on the planimetric (x, y) positional accuracy of the National Shoreline (NSL) data, produced in collaboration between the Swedish mapping agency Lantmäteriet and the Swedish Maritime Administration (SMA). Due to the compound nature of shorelines, such data is afflicted by substantial positional uncertainties. In contrast, the positional accuracy requirements of NSL data are high. An apparent problem is that Lantmäteriet do not measure the positional accuracy of NSL in accordance to the NSL data product specification. In addition, currently, there is little understanding of the latent positional changes of shorelines affected by the component of time, in direct influence of the accuracy of NSL. Therefore, in accordance to the two specific aims of this study, first, an accuracy assessment technique is applied so that to measure the positional accuracy of NSL. Second, positional time changes of NSL are analysed. This study provides with an overview of potential problems and future prospects of NSL, which can be used by Lantmäteriet to improve the quality assurance of the data. Two line-based NSL data sets within the NSL classified regions of Sweden are selected. Positional uncertainties of the selected NSL areas are investigated using two distinctive methodologies. First, an accuracy assessment method is applied and accuracy metrics by the root-means-square error (RMSE) are derived. The accuracy metrics are checked toward specification and standard accuracy tolerances. Results of the assessment by the calculated RMSE metrics in comparison to tolerances indicate on an approved accuracy of tested data. Second, positional changes of NSL data are measured using a proposed space-time analysis technique. The results of the analysis reveal significant discrepancies between the two areas investigated, indicating that one of the test areas are influenced by much greater positional changes over time. The accuracy assessment method used in this study has a number of apparent constraints. One manifested restriction is the potential presence of bias in the derived accuracy metrics. In mind of current restrictions, the method to be preferred in assessment of the positional accuracy of NSL is a visual inspection towards aerial photographs. In regard of the result of the space-time analysis, one important conclusion can be made. Time-dependent positional discrepancies between the two areas investigated, indicate that Swedish coastlines are affected by divergent degrees of positional changes over time. Therefore, Lantmäteriet should consider updating NSL data at different time phases dependent on the prevailing regional changes so that to assure the currently specified positional accuracy of the entire data structure of NSL.
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Abdelhafeid, Faraj. "The Effect Upon Antenna Arrays of Variations of Element Orientation and Spacing in the Presence of Channel Noise, with an Application to Direction Finding." University of Dayton / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1525866099535246.

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Cantarello, Luca. "Analisi delle previsioni meteorologiche mensili mediante il modello GLOBO (ISAC-CNR)." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amslaurea.unibo.it/7690/.

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In questo lavoro sono presentate le principali caratteristiche delle previsioni meteorologiche mensili, nonché il progresso scientifico e storico che le ha coinvolte e le tecniche adibite alla loro verifica. Alcune di queste tecniche sono state applicate al fine di valutare ed analizzare l'errore sistematico (o bias) e l'RMSE di temperatura a 850 hPa (T850), altezza geopotenziale a 500 hPa (Z500) e precipitazioni cumulate del modello GLOBO, utilizzato presso l'Istituto per le Scienze dell'Atmosfera e del Clima del Consiglio Nazionale delle Ricerche per formulare previsioni mensili. I risultati mostrano la progressione temporale dell'errore, che aumenta nelle prime due settimane di integrazione numerica fino a stabilizzarsi tra la terza e la quarta. Ciò mostra che il modello, persa l'influenza delle condizioni iniziali, raggiunge un suo stato che, per quanto fisiologicamente distante da quello osservato, tende a stabilizzarsi e a configurarsi quindi come sistematico (eventualmente facilitandone la rimozione in fase di calibrazione delle previsioni). Il bias di T850 e Z500 presenta anomalie negative prevalentemente lungo le zone equatoriali, e vaste anomalie positive sulle aree extra-tropicali; quello delle precipitazioni mostra importanti sovrastime nelle zone continentali tropicali. La distribuzione geografica dell'RMSE (valutato solo per T850 e Z500) riscontra una generale maggiore incertezza nelle zone extra-tropicali, specie dell'emisfero settentrionale e nei mesi freddi.
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Mansour, Tony, and Majdi Murtaja. "Applied estimation theory on power cable as transmission line." Thesis, Linnéuniversitetet, Institutionen för fysik och elektroteknik (IFE), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-46583.

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This thesis presents how to estimate the length of a power cable using the MaximumLikelihood Estimate (MLE) technique by using Matlab. The model of the power cableis evaluated in the time domain with additive white Gaussian noise. The statistics havebeen used to evaluate the performance of the estimator, by repeating the experiment fora large number of samples where the random additive noise is generated for each sample.The estimated sample variance is compared to the theoretical Cramer Raw lower Bound(CRLB) for unbiased estimators. At the end of thesis, numerical results are presentedthat show when the resulting sample variance is close to the CRLB, and hence that theperformance of the estimator will be more accurate.
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Reigota, Nilvana dos Santos. "Comparação da transformada wavelet discreta e da transformada do cosseno, para compressão de imagens de impressão digital." Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/18/18152/tde-27042007-101810/.

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Este trabalho tem por objetivo comparar os seguintes métodos de compressão de imagens de impressão digital: transformada discreta do cosseno (DCT), transformada de wavelets de Haar, transformada de wavelets de Daubechies e transformada de wavelets de quantização escalar (WSQ). O propósito da comparação é identificar o método que resulta numa menor perda de dados, para a maior taxa de compressão possível. São utilizadas as seguintes métricas para avaliação da qualidade da imagem para os métodos: erro quadrático médio (ERMS), a relação sinal e ruído (SNR) e a relação sinal ruído de pico (PSNR). Para as métricas utilizadas a DCT apresentou os melhores resultados, seguida pela WSQ. No entanto, o melhor tempo de compressão e a melhor qualidade das imagens recuperadas avaliadas pelo software GrFinger 4.2, foram obtidos com a técnica WSQ.
This research aims to compare the following fingerprint image compression methods: the discrete cosseno transform (DCT), Haar wavelet transform, Daubechies wavelets transform and wavelet scalar quantization (WSQ). The main interest is to find out the technique with the smallest distortion and higher compression ratio. Image quality is measured using peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR) and root mean square (ERMS). Image quality using these metrics showed best results for the DCT followed by WSQ, although the WSQ had the best compression time and presented the best quality when evaluated by the GrFinger 4.2 software.
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Khurram, Jassal Muhammad. "The Effect of Optimization of Error Metrics." Thesis, Högskolan i Borås, Institutionen Handels- och IT-högskolan, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-20471.

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It is important for a retail company to forecast its sale in correct and accurate way to be ableto plan and evaluate sales and commercial strategies. Various forecasting techniques areavailable for this purpose. Two popular modelling techniques are Predictive Modelling andEconometric Modelling. The models created by these techniques are used to minimize thedifference between the real and the predicted values. There are several different errormetrics that can be used to measure and describe the difference. Each metric focuses ondifferent properties in the forecasts and it is hence important which metrics that is used whena model is created. Most traditional techniques use the sum of squared error which havegood mathematical properties but is not always optimal for forecasting purposes. This thesisfocuses on optimization of three widely used error metrics MAPE, WMAPE and RMSE.Especially the metrics protection against overfitting, which occurs when a predictive modelcatches noise and irregularities in the data, that is not part of the sought relationship, isevaluated in this thesis.Genetic Programming, a general optimization technique based on Darwin’s theories ofevolution. In this study genetic programming is used to optimize predictive models based oneach metrics. The sales data of five products of ICA (a Swedish retail company) has beenused to observe the effects of the optimized error metrics when creating predictive models.This study shows that all three metrics are quite poorly protected against overfitting even ifWMAPE and MAPE are slightly better protected than MAPE. However WMAPE is the mostpromising metric to use for optimization of predictive models. When evaluated against allthree metrics, models optimized based on WMAPE have the best overall result. The results oftraining and test data shows that the results hold in spite of overfitted models.
Program: Magisterutbildning i informatik
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Laskauskas, Ramūnas. "Vaizdo kontūrų nustatymo būdų analizė." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2008. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2008~D_20080929_113638-76811.

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Vaizdo kontūrų nustatymo metodų tyrimui buvo pasirinktas 100 įvairaus turinio paveikslų su įvairiu elementų dydžiu ir skaičiumi. Tyrimui buvo pasirinkti 8 populiariausi vaizdo kontūrų nustatymo metodai: Canny, Sobel, Prewitt, Roberts, Zerocross, Laplacian, LoG, Marr-Hildreth. Atliekant tyrimus visiems paveikslams, naudojant visus 8 metodus, buvo subjektyviai parinkta optimaliausia slenkstinė reikšmė. Gavus visų 100 įvairių paveikslų geriausias slenkstines reikšmes su visais 8 metodais, buvo nustatytos slenkstinių reikšmių kitimo ribos kiekvienam kontūro išskyrimo metodui. Kiekvienam paveikslui buvo pritaikyta vidutiniškai 10 slenkstinių reikšmių ir kiekvienam paveikslui buvo suskaičiuotas vidutinis kvadratinis nuokrypis (RMSE, Root Mean Square Error) su geriausiu pasirinktu kontūru.
One hundred various pictures with different size and number of elements were chosen for the method research of image outline evaluation. All these pictures were converted into grayscale pictures. Most of edge detection methods (filters) required to be blurred to reduce noise. Eight the most popular methods were chosen to evaluate the image outline: Canny, Sobel, Prewitt, Roberts, Zerocross, Laplacian, LoG, Marr-Hildreth. A Root Mean Square Error (RMSE) was computed for each edge picture with the best-chosen outline.
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Morelli, Stefano. "Optimal pose selection for the calibration of an overconstrained Cable-Driven Parallel Robot." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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In this project an optimal pose selection method for the calibration of an overconstrained Cable-Driven Parallel robot is presented. This manipulator belongs to a subcategory of parallel robots, where the classic rigid "legs" are replaced by cables. Cables are flexible elements that bring advantages and disadvantages to the robot modeling. For this reason, there are many open research issues, and the calibration of geometric parameters is one of them. The identification of the geometry of a robot, in particular, is usually called Kinematic Calibration. Many methods have been proposed in the past years for the solution of the latter problem. Although these methods are based on calibration using different kinematic models, when the robot’s geometry becomes more complex, their robustness and reliability decrease. This fact makes the selection of the calibration poses more complicated. The position and the orientation of the endeffector in the workspace become important in terms of selection. Thus, in general, it is necessary to evaluate the robustness of the chosen calibration method, by means, for example, of a parameter such as the observability index. In fact, it is known from the theory, that the maximization of the above mentioned index identifies the best choice of calibration poses, and consequently, using this pose set may improve the calibration process. The objective of this thesis is to analyze optimization algorithms which aim to calculate an optimal choice of poses both in quantitative and qualitative terms. Quantitatively, because it is of fundamental importance to understand how many poses are needed. Not necessarily a greater number of poses leads to a better result. Qualitatively, because it is useful to understand if the selected combination of poses actually gives additional information in the process of the identification of the parameters.
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Books on the topic "RMSE"

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Minkov, Todor. Dve pobedi na RMS: Pŭrvii͡a︡t kongres na RMS, prot͡s︡esŭt sreshtu T͡S︡K na RMS. Sofii͡a︡: "Nar. mladezh", 1989.

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Chen, Derek Hung Chiat. The rmsm-x+p: A minimal poverty module for the rmsm-x. [Washington, D.C: World Bank, 2004.

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RMS Queen Mary. Minneapolis, MN: Bellwether Media, Inc., 2015.

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Scheiblich, Reinhard. Leuchttu˜rme-Lexikon. 4th ed. Hamburg: Ed. Ellert & Richter, 2010.

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Cooper, Suzanne Tarbell. RMS Queen Mary. Charleston, S.C: Arcadia Pub., 2010.

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Stoĭkov, Mari͡an. Nashii͡at RMS: Spomeni. Sofii͡a: Nar. mladezh, 1989.

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Everaert, Luc. A RMSM-X model for Turkey. Washington, DC: Country Economics Dept., World Bank, 1990.

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4 rms w vu. Woodstock, NY: Mayapple Press, 2014.

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Hind, Andrew. RMS Segwun: Queen of Muskoka. Toronto: Dundurn, 2012.

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RMS Titanic: A modern legend. Southampton: Kingfisher Railway Productions, 1987.

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Book chapters on the topic "RMSE"

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Su, Yong, and Qingchuan Zhang. "Quality Assessment of Speckle Patterns by Estimating RMSE." In International Digital Imaging Correlation Society, 71–74. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51439-0_17.

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Li, Wang, Xiaoan Tang, and Junda Zhang. "SRG and RMSE-Based Automated Segmentation for Volume Data." In Lecture Notes in Computer Science, 194–203. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71598-8_18.

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Haut, Nathan, Wolfgang Banzhaf, and Bill Punch. "Correlation Versus RMSE Loss Functions in Symbolic Regression Tasks." In Genetic and Evolutionary Computation, 31–55. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8460-0_2.

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Adenuga, Olukorede Tijani, Khumbulani Mpofu, and Ragosebo Kgaugelo Modise. "Application of ARIMA-LSTM for Manufacturing Decarbonization Using 4IR Concepts." In Lecture Notes in Mechanical Engineering, 115–23. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18326-3_12.

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AbstractIncreasing climate change concerns call for the manufacturing sector to decarbonize its process by introducing a mitigation strategy. Energy efficiency concepts within the manufacturing process value chain are proportional to the emission reductions, prompting decision makers to require predictive tools to execute decarbonization solutions. Accurate forecasting requires techniques with a strong capability for predicting automotive component manufacturing energy consumption and carbon emission data. In this paper we introduce a hybrid autoregressive moving average (ARIMA)-long short-term memory network (LSTM) model for energy consumption forecasting and prediction of carbon emission within the manufacturing facility using the 4IR concept. The method could capture linear features (ARIMA) and LSTM captures the long dependencies in the data from the nonlinear time series data patterns, Root means square error (RMSE) is used for data analysis comparing the performance of ARIMA which is 448.89 as a single model with ARIMA-LSTM hybrid model as actual (trained) and predicted (test) 59.52 and 58.41 respectively. The results depicted RMSE values of ARIMA-LSTM being extremely smaller than ARIMA, which proves that hybrid ARIMA-LSTM is more suitable for prediction than ARIMA.
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Ko’adan, Mohammed A., Mohammed A. Bamatraf, and Khalid Q. Shafal. "Clustering Analysis to Improve Web Search Ranking Using PCA and RMSE." In Advances on Smart and Soft Computing, 93–105. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6048-4_9.

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Kobialka, Hans-Ulrich. "Friction estimation – optimization of sensor configuration with respect to RMSE and costs." In Proceedings, 741–55. Wiesbaden: Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-658-05978-1_52.

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Akeh, Ugbah Paul, Steve Woolnough, and Olumide A. Olaniyan. "ECMWF Subseasonal to Seasonal Precipitation Forecast for Use as a Climate Adaptation Tool Over Nigeria." In African Handbook of Climate Change Adaptation, 1613–30. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_97.

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AbstractFarmers in most parts of Africa and Asia still practice subsistence farming which relies minly on seasonal rainfall for Agricultural production. A timely and accurate prediction of the rainfall onset, cessation, expected rainfall amount, and its intra-seasonal variability is very likely to reduce losses and risk of extreme weather as well as maximize agricultural output to ensure food security.Based on this, a study was carried out to evaluate the performance of the European Centre for Medium-range Weather Forecast (ECMWF) numerical Weather Prediction Model and its Subseasonal to Seasonal (S2S) precipitation forecast to ascertain its usefulness as a climate change adaptation tool over Nigeria. Observed daily and monthly CHIRPS reanalysis precipitation amount and the ECMWF subseasonal weekly precipitation forecast data for the period 1995–2015 was used. The forecast and observed precipitation were analyzed from May to September while El Nino and La Nina years were identified using the Oceanic Nino Index. Skill of the forecast was determined from standard metrics: Bias, Root Mean Square Error (RMSE), and Anomaly Correlation Coefficient (ACC).The Bias, RMSE, and ACC scores reveal that the ECMWF model is capable of predicting precipitation over Southern Nigeria, with the best skill at one week lead time and poorest skills at lead time of 4 weeks. Results also show that the model is more reliable during El Nino years than La-Nina. However, some improvement in the model by ECMWF can give better results and make this tool a more dependable tool for disaster risk preparedness, reduction and prevention of possible damages and losses from extreme rainfall during the wet season, thus enhancing climate change adaptation.
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Gong, Fengxun, and Ma Yanqiu. "Analysis of Positioning Performance of the Algorithm of Time Sum of Arrival with RMSE." In Communications in Computer and Information Science, 579–91. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-7305-2_49.

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Han, Bo, Bo He, Mengmeng Ma, Tingting Sun, Tianhong Yan, and Amaury Lendasse. "RMSE-ELM: Recursive Model Based Selective Ensemble of Extreme Learning Machines for Robustness Improvement." In Proceedings of ELM-2014 Volume 1, 273–92. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14063-6_24.

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Chowdary, Nama Deepak, Tadepally Hrushikesh, Kusampudi Madhava Varma, and Shaik Ali Basha. "Time Series Analysis and Forecast Accuracy Comparison of Models Using RMSE–Artificial Neural Networks." In Advances in Intelligent Systems and Computing, 317–25. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0550-8_26.

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Conference papers on the topic "RMSE"

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Mongia, Hukam C. "N+3 and N+4 Generation Aeropropulsion Engine Combustors: Part 3 — Small Engines’ Emissions and Axial Staging Combustion Technology." In ASME Turbo Expo 2013: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/gt2013-94572.

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Comprehensive assessment of the small rich-dome engines was conducted leading to the following emissions correlations: NOxEI LEC = 0.02991 × OPR1.9791 RMSE = 3.0% NOxEI TALON II = 0.01666 × OPR2.1403 RMSE = 2.0% NOxEI CFM TI = 0.06763 × OPR1.7458 RMSE = 2.1% NOxEI CF34 = 0.0541 × OPR1.7917R2 = 0.9794 RMSE = 2.4% NOxEI SM = 0.04782 × OPR1.8388 RMSE = 4.2% NOxEI All = 0.03856 × OPR1.9058 RMSE = 3.9% The best of the small engines’ gaseous emissions, albeit at lower takeoff pressure ratios, were shown to be very competitive with the best of medium and large size engines. Axially-staged combustion with partially premixed jets in crossflow was identified as a promising concept to pursue for the (N+3) technology mixers.
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Kuan, Sao-I., Jongmin Kim, Oh-Heum Kwon, and Ha-Joo Song. "Canopy�K-means Combined Collaborative Filtering Using RMSE-minimization." In 2022 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE, 2022. http://dx.doi.org/10.1109/bigcomp54360.2022.00016.

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Chao, Paul C. P., and Pei-Yu Chiang. "Photoplethysmography Signals Processing Using Polynomial Profile Fitting for Measuring the Blood Flow Volume in Arteriovenous Fistula." In ASME 2017 Conference on Information Storage and Processing Systems collocated with the ASME 2017 International Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Microsystems. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/isps2017-5445.

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In this work, a new photoplethysmography (PPG) signal processing methodology using polynomial profile fitting for measuring blood flow volume (BFV) in arteriovenous fistula (AVF) is proposed. After calibration, the experiment results using proposed method shows higher correlation (R = 0.7883) and lower error (RMSE = 109 ml/min) compared to the ones using conventional methods (R = 0.3212, RMSE = 168 ml/min).
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Patni, Hrithik, Akash Jagtap, Vaishali Bhoyar, and Aditya Gupta. "Speech Emotion Recognition using MFCC, GFCC, Chromagram and RMSE features." In 2021 8th International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 2021. http://dx.doi.org/10.1109/spin52536.2021.9566046.

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Fang, Qiang. "Is average RMSE appropriate for evaluating acoustic-to-articulatory inversion?" In 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2019. http://dx.doi.org/10.1109/apsipaasc47483.2019.9023269.

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Oliveira Junior, Adair da Silva, Marcio Carneiro Brito Pache, Fábio Prestes Cesar Rezende, Diego André Sant’Ana, Vanessa Aparecida de Moraes Weber, Gilberto Astolfi, Fabricio de Lima Weber, et al. "An Investigation of Parameter Optimization in Fingerling Counting Problems." In Workshop de Visão Computacional. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/wvc.2021.18881.

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The objective of this paper is to investigate which combination of parameters for the fingerling counting software results in the smallest Mean Absolute Error (MAE) and smallest Root Mean Squared Error (RMSE). For this, an image dataset called FISHCV155V was created and separated into training and test sets, where different combinations of parameters for the software were tested. From the obtained results were extracted individual performance metrics for each combination of parameters, such as MAE, Mean Square Error (MSE) and RMSE. Video frames were analysed comparing the parameter combination that obtained the best and worst results, in order to investigate the influence of such parameters in the performance of the software. From such results, it was concluded that the best combination reached 5.99 MAE and 9.96 RMSE.
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Almeida, Anderson, Marcos Amaris, and Bruno Merlin. "Predição temporal e espaço-temporal dos parâmetros da qualidade da água." In Escola Regional de Alto Desempenho Norte 2. Sociedade Brasileira de Computação, 2021. http://dx.doi.org/10.5753/erad-no2.2021.18676.

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A qualidade da água está diretamente relacionada com o seu nível de poluição causada pelas ações antrópicas e industriais. Por isso, são realizados os monitoramentos limnológicos dos parâmetros básicos da qualidade da água, como forma de obtenção de dados que norteiam as tomadas de decisão dos órgãos gestores de recursos hídricos. Neste contexto, o presente estudo tem o objetivo de analisar o conjunto de dados e o desempenho dos algoritmos regressão linear, random forest, redes neurais MLP e LSTM na predição temporal e espaço-temporal. Os modelos são avaliados através das métricas MAPE e RMSE. Portanto, na predição temporal a técnica LSTM apresenta o menor MAPE médio, 4.66% e o MLP o menor RMSE médio, 2.47. Porém, na predição espaço-temporal, o MLP tem o menor resultado médio de MAPE e RMSE, respectivamente, 5.94% e 1.34.
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Chávez, Manuel, Israel Chávez, Eduardo Torres, Sandro Atoche, Stefano Palacios, Luis Trelles, Cristhian Aldana, Yesenia Saavedra, Gustavo Mendoza, and Nelson Chuquihuanca. "Detection of Outliers in The Peruvian Fruit Production Time Series Using Arima Models." In Intelligent Human Systems Integration (IHSI 2022) Integrating People and Intelligent Systems. AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001008.

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The present applied, non-experimental, descriptive and prognostic research; was aimed at detecting outliers in the agricultural production of Mangifera indica (mango), Persea americana (avocado) and Citrus x aurantifolia (lemon) at the national level, was performed by applying an ARIMA Model. To fulfill it purposes, documentary analysis was used at the National Institute of Statistics and Informatics (In Spanish, INEI). The study sample consisted of the mango, avocado and lemon production indices 2000-2020. As a result, the models were obtained arima mango (1,0,0) (2,1,2) (AIC=5448.99, BIC=5473.35 and RMSE=19067.93), arima avocado (0,1,3) (2,1,0) (AIC=4687.05, BIC=4707.91 and RMSE=4114.35) and arima lemon (1,0,1) (0,1,1) (AIC=4484.36, BIC=4501.76 and RMSE=2551.96) with a 12 months period, the diagram of boxes and whiskers was also made with which it was identified that atypical data (Outliers) abound in the periods of greatest production.
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Pinto, Breno, Varin Khera, and Chun Che Fung. "Detecting security anomalies from internet traffic using the MA-RMSE algorithms." In 2009 7th IEEE International Conference on Industrial Informatics (INDIN). IEEE, 2009. http://dx.doi.org/10.1109/indin.2009.5195920.

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Almeida, Anderson, Marcos Amaris, Bruno Merlin, and Allan Veras. "Modelagem e Predição temporal de Parâmetros de Qualidade de Água usando Redes Neurais Profundas." In Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/wcama.2020.11026.

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A qualidade da água está diretamente relacionada com o seu nível de poluição, e para isso, é necessário o monitoramento para identificar as características físicas, químicas e biológicas, considerando a legislação vigente. Este artigo apresenta a comparação dos modelos de rede neural Long-Short Term Memory (LSTM) e Perceptron Multilayer (MLP) para predizer os parâmetros pH, OD, DBO, Fósforo e Turbidez da qualidade da água. Foram usadas as métricas de erro RMSE e MSE, quando as redes neurais são configuradas com 10, 25 e 50 neurônios. A rede LSTM apresentou um RMSE médio de 0,134, MSE médio de 0,035 e MAPE médio de 13,49. A rede MLP apresentou RMSE médio de 0,085, MSE médio de 0,01 e MAPE médio de 13,03. Os resultados do experimentos visam contribuir com o processo de monitoramento da qualidade da água e auxiliar o planejamento da gestão hídrica através do modelo de aprendizado de máquina adequado para predição dos parâmetros.
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Reports on the topic "RMSE"

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Brodie, Katherine, Brittany Bruder, Richard Slocum, and Nicholas Spore. Simultaneous mapping of coastal topography and bathymetry from a lightweight multicamera UAS. Engineer Research and Development Center (U.S.), August 2021. http://dx.doi.org/10.21079/11681/41440.

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A low-cost multicamera Unmanned Aircraft System (UAS) is used to simultaneously estimate open-coast topography and bathymetry from a single longitudinal coastal flight. The UAS combines nadir and oblique imagery to create a wide field of view (FOV), which enables collection of mobile, long dwell timeseries of the littoral zone suitable for structure-from motion (SfM), and wave speed inversion algorithms. Resultant digital surface models (DSMs) compare well with terrestrial topographic lidar and bathymetric survey data at Duck, NC, USA, with root-mean-square error (RMSE)/bias of 0.26/–0.05 and 0.34/–0.05 m, respectively. Bathymetric data from another flight at Virginia Beach, VA, USA, demonstrates successful comparison (RMSE/bias of 0.17/0.06 m) in a secondary environment. UAS-derived engineering data products, total volume profiles and shoreline position, were congruent with those calculated from traditional topo-bathymetric surveys at Duck. Capturing both topography and bathymetry within a single flight, the presented multicamera system is more efficient than data acquisition with a single camera UAS; this advantage grows for longer stretches of coastline (10 km). Efficiency increases further with an on-board Global Navigation Satellite System–Inertial Navigation System (GNSS-INS) to eliminate ground control point (GCP) placement. The Appendix reprocesses the Virginia Beach flight with the GNSS–INS input and no GCPs.
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Conery, Ian, Brittany Bruder, Connor Geis, Jessamin Straub, Nicholas Spore, and Katherine Brodie. Applicability of CoastSnap, a crowd-sourced coastal monitoring approach for US Army Corps of Engineers district use. Engineer Research and Development Center (U.S.), September 2023. http://dx.doi.org/10.21079/11681/47568.

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This US Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory, technical report details the pilot deployment, accuracy evaluation, and best practices of the citizen-science, coastal-image monitoring program CoastSnap. Despite the need for regular observational data, many coastlines are monitored infrequently due to cost and personnel, and this cell phone-image-based approach represents a new potential data source to districts in addition to providing an outreach opportunity for the public. Requiring minimal hardware and signage, the system is simple to install but requires user-image processing. Analysis shows the CoastSnap-derived shorelines compare well to real-time kinematic and lidar-derived shorelines during low-to-moderate wave conditions (root mean square errors [RMSEs] <10 m). During high-wave conditions, errors are higher (RMSE up to 18 m) but are improved when incorporating wave run-up. Beyond shoreline quantification, images provide other qualitative information such as storm-impact characteristics and timing of the formation of beach scarps. Ultimately, the citizen-science tool is a viable low-cost option to districts for monitoring shorelines and tracking the evolution of coastal projects such as beach nourishments.
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Ermolayev, Vadim, Frédéric Mallet, Vitaliy Yakovyna, Vyacheslav Kharchenko, Vitaliy Kobets, Artur Korniłowicz, Hennadiy Kravtsov, Mykola Nikitchenko, Сергій Олексійович Семеріков, and Aleksander Spivakovsky, eds. ICTERI 2019: ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer : Proceedings of the 15th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer. Volume II: Workshops. Kherson, Ukraine, June 12-15, 2019. CEUR Workshop Proceedings, June 2019. http://dx.doi.org/10.31812/123456789/3170.

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This volume represents the proceedings of the Workshops co-located with the 15th International Conference on ICT in Education, Research, and Industrial Applications, held in Kherson, Ukraine, in June 2019. It comprises 82 contributed papers that were carefully peer-reviewed and selected from 218 submissions for the five workshops: 3L-Person, CoSinE, ITER, RMSE, and TheRMIT. The volume is structured in five parts, each presenting the contributions for a particular workshop. The topical scope of the volume is aligned with the thematic tracks of ICTERI 2019: (I) Advances in ICT Research; (II) Information Systems: Technology and Applications; (III) ICT in Education; and (IV) ICT Cooperation in Academia and Industry.
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Pompeu, Gustavo, and José Luiz Rossi. Real/Dollar Exchange Rate Prediction Combining Machine Learning and Fundamental Models. Inter-American Development Bank, September 2022. http://dx.doi.org/10.18235/0004491.

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The study of the predictability of exchange rates has been a very recurring theme on the economics literature for decades, and very often is not possible to beat a random walk prediction, particularly when trying to forecast short time periods. Although there are several studies about exchange rate forecasting in general, predictions of specifically Brazilian real (BRL) to United States dollar (USD) exchange rates are very hard to find in the literature. The objective of this work is to predict the specific BRL to USD exchange rates by applying machine learning models combined with fundamental theories from macroeconomics, such as monetary and Taylor rule models, and compare the results to those of a random walk model by using the root mean squared error (RMSE) and the Diebold-Mariano (DM) test. We show that it is possible to beat the random walk by these metrics.
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Pradhan, Nawa Raj. Estimating growing-season root zone soil moisture from vegetation index-based evapotranspiration fraction and soil properties in the Northwest Mountain region, USA. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/42128.

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A soil moisture retrieval method is proposed, in the absence of ground-based auxiliary measurements, by deriving the soil moisture content relationship from the satellite vegetation index-based evapotranspiration fraction and soil moisture physical properties of a soil type. A temperature–vegetation dryness index threshold value is also proposed to identify water bodies and underlying saturated areas. Verification of the retrieved growing season soil moisture was performed by comparative analysis of soil moisture obtained by observed conventional in situ point measurements at the 239-km2 Reynolds Creek Experimental Watershed, Idaho, USA (2006–2009), and at the US Climate Reference Network (USCRN) soil moisture measurement sites in Sundance, Wyoming (2012–2015), and Lewistown, Montana (2014–2015). The proposed method best represented the effective root zone soil moisture condition, at a depth between 50 and 100 cm, with an overall average R2 value of 0.72 and average root mean square error (RMSE) of 0.042.
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Patwa, B., P. L. St-Charles, G. Bellefleur, and B. Rousseau. Predictive models for first arrivals on seismic reflection data, Manitoba, New Brunswick, and Ontario. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/329758.

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First arrivals are the primary waves picked and analyzed by seismologists to infer properties of the subsurface. Here we try to solve a problem in a small subsection of the seismic processing workflow: first break picking of seismic reflection data. We formulate this problem as an image segmentation task. Data is preprocessed, cleaned from outliers and extrapolated to make the training of deep learning models feasible. We use Fully Convolutional Networks (specifically UNets) to train initial models and explore their performance with losses, layer depths, and the number of classes. We propose to use residual connections to improve each UNet block and residual paths to solve the semantic gap between UNet encoder and decoder which improves the performance of the model. Adding spatial information as an extra channel helped increase the RMSE performance of the first break predictions. Other techniques like data augmentation, multitask loss, and normalization methods, were further explored to evaluate model improvement.
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Letcher, Theodore, Julie Parno, Zoe Courville, Lauren Farnsworth, and Jason Olivier. A generalized photon-tracking approach to simulate spectral snow albedo and transmittance using X-ray microtomography and geometric optics. Engineer Research and Development Center (U.S.), June 2023. http://dx.doi.org/10.21079/11681/47122.

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A majority of snow radiative transfer models (RTMs) treat snow as a collection of idealized grains rather than an organized ice–air matrix. Here we present a generalized multi-layer photon-tracking RTM that simulates light reflectance and transmittance of snow based on X-ray micro- tomography images, treating snow as a coherent 3D structure rather than a collection of grains. The model uses a blended approach to expand ray-tracing techniques applied to sub-1 cm3 snow samples to snowpacks of arbitrary depths. While this framework has many potential applications, this study’s effort is focused on simulating reflectance and transmittance in the visible and near infrared (NIR) through thin snow- packs as this is relevant for surface energy balance and remote sensing applications. We demonstrate that this framework fits well within the context of previous work and capably reproduces many known optical properties of a snow surface, including the dependence of spectral reflectance on the snow specific surface area and incident zenith angle as well as the surface bidirectional reflectance distribution function (BRDF). To evaluate the model, we compare it against reflectance data collected with a spectroradiometer at a field site in east-central Vermont. In this experiment, painted panels were inserted at various depths beneath the snow to emulate thin snow. The model compares remarkably well against the reflectance measured with a spectroradiometer, with an average RMSE of 0.03 in the 400–1600 nm range. Sensitivity simulations using this model indicate that snow transmittance is greatest in the visible wavelengths, limiting light penetration to the top 6 cm of the snowpack for fine-grain snow but increasing to 12 cm for coarse-grain snow. These results suggest that the 5% transmission depth in snow can vary by over 6 cm according to the snow type.
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Smartt, Heidi A., and Steven Hammon. Energy Harvesting RMSA Field Test. Office of Scientific and Technical Information (OSTI), September 2018. http://dx.doi.org/10.2172/1481636.

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Hammon, Steven, and Heidi A. Smartt. Energy Harvesting RMSA Field Test. Office of Scientific and Technical Information (OSTI), October 2018. http://dx.doi.org/10.2172/1531314.

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Guarini, William. Remote Minehunting System (RMS). Fort Belvoir, VA: Defense Technical Information Center, November 2015. http://dx.doi.org/10.21236/ad1019510.

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