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Journal articles on the topic 'River flow time series'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Samsudin, R., P. Saad, and A. Shabri. "River flow time series using least squares support vector machines." Hydrology and Earth System Sciences 15, no. 6 (2011): 1835–52. http://dx.doi.org/10.5194/hess-15-1835-2011.

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Abstract. This paper proposes a novel hybrid forecasting model known as GLSSVM, which combines the group method of data handling (GMDH) and the least squares support vector machine (LSSVM). The GMDH is used to determine the useful input variables which work as the time series forecasting for the LSSVM model. Monthly river flow data from two stations, the Selangor and Bernam rivers in Selangor state of Peninsular Malaysia were taken into consideration in the development of this hybrid model. The performance of this model was compared with the conventional artificial neural network (ANN) models,

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Nigam, Rashmi, Sohail Bux, Sudhir Nigam, K. R. Pardasani, S. K. Mittal, and Ruhi Haque. "Time series modeling and forecast of river flow." Current World Environment 4, no. 1 (2009): 79–87. http://dx.doi.org/10.12944/cwe.4.1.11.

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Nazir, Hafiza Mamona, Ijaz Hussain, Ishfaq Ahmad, Muhammad Faisal, and Ibrahim M. Almanjahie. "An improved framework to predict river flow time series data." PeerJ 7 (July 1, 2019): e7183. http://dx.doi.org/10.7717/peerj.7183.

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Due to non-stationary and noise characteristics of river flow time series data, some pre-processing methods are adopted to address the multi-scale and noise complexity. In this paper, we proposed an improved framework comprising Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Empirical Bayesian Threshold (CEEMDAN-EBT). The CEEMDAN-EBT is employed to decompose non-stationary river flow time series data into Intrinsic Mode Functions (IMFs). The derived IMFs are divided into two parts; noise-dominant IMFs and noise-free IMFs. Firstly, the noise-dominant IMFs are denoised using

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4

Hutton, Jane L. "Non-negative time series models for dry river flow." Journal of Applied Probability 27, no. 1 (1990): 171–82. http://dx.doi.org/10.2307/3214604.

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Non-negative time series which are first-order autoregressive with a mixed exponential innovation process are studied. Properties and approximate marginal distributions for such series are found. Modifications to include exact zeroes and to increase variability so that the time series is a more realistic model of rivers which are dry for part of the year are discussed.

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Hutton, Jane L. "Non-negative time series models for dry river flow." Journal of Applied Probability 27, no. 01 (1990): 171–82. http://dx.doi.org/10.1017/s0021900200038511.

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Non-negative time series which are first-order autoregressive with a mixed exponential innovation process are studied. Properties and approximate marginal distributions for such series are found. Modifications to include exact zeroes and to increase variability so that the time series is a more realistic model of rivers which are dry for part of the year are discussed.

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PORPORATO, AMILCARE, and LUCA RIDOLFI. "Detecting determinism and nonlinearity in river-flow time series." Hydrological Sciences Journal 48, no. 5 (2003): 763–80. http://dx.doi.org/10.1623/hysj.48.5.763.51457.

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Krishna, Budu, Yellamelli Ramji Satyaji Rao, and Purna Chandra Nayak. "Wavelet neural network model for river flow time series." Proceedings of the Institution of Civil Engineers - Water Management 165, no. 8 (2012): 425–39. http://dx.doi.org/10.1680/wama.10.00092.

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Bordignon, S., and F. Lisi. "Nonlinear analysis and prediction of river flow time series." Environmetrics 11, no. 4 (2000): 463–77. http://dx.doi.org/10.1002/1099-095x(200007/08)11:4<463::aid-env429>3.0.co;2-#.

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Đurin, Bojan, Mirna Raič, Petra Sušilović, and Hossein Banejad. "Analysis of homogeneity and isotropy of the flow in the watercourses by applying the RAPS and IPTA methods." Advances in Civil and Architectural Engineering 15, no. 29 (2024): 67–83. http://dx.doi.org/10.13167/2024.29.5.

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Due to the frequent climatic changes occurring worldwide, which are related to extreme meteorological parameters as well as human activities, it is obvious that these influence the flow regimes of rivers. River flow is the most important factor determining the hydrological regime of any river. This has a substantial influence on the water resources and the environment surrounding the river. Hydrotechnical structures are also dimensioned on the basis of the flow as the primary input parameter. The flow conditions have different properties and correlations with the material of the river bed. In

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Ahmad, Hassan Syed, and Nazia Munawar. "Impact Assessment of Hydroclimatic and Stochastic Variations on Water Stress of Jhelum River Flow Forecast." International Journal of Sciences Volume 8, no. 2019-06 (2019): 80–90. https://doi.org/10.5281/zenodo.3350763.

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The hydrological system of a country significantly influences the ecological, agricultural and consequently the economic structure. Since, the accessibility of water resources has governed the power generation sector and agronomic establishments of the region like Pakistan. Moreover, Pakistan is sensitive in climatic variation and faces gradually higher threats regarding rainfall variability, floods and prolonged droughts. Sometimes, these climate change impact embedded with the high frequency and strength of extreme events caused by global warming. These extreme situations occur mostly in the

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Whitehead, P. G., A. J. Wade, and D. Butterfield. "Potential impacts of climate change on water quality and ecology in six UK rivers." Hydrology Research 40, no. 2-3 (2009): 113–22. http://dx.doi.org/10.2166/nh.2009.078.

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A modelling study has been undertaken to assess the likely impacts of climate change on water quality across the UK. A range of climate change scenarios have been used to generate future precipitation, evaporation and temperature time series at a range of catchments across the UK. These time series have then been used to drive the Integrated Catchment (INCA) suite of flow, water quality and ecological models to simulate flow, nitrate, ammonia, total and soluble reactive phosphorus, sediments, macrophytes and epiphytes in the Rivers Tamar, Lugg, Tame, Kennet, Tweed and Lambourn. A wide range of

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Alizadeh, Mohamad Javad, Vahid Nourani, Mojtaba Mousavimehr, and Mohamad Reza Kavianpour. "Wavelet-IANN model for predicting flow discharge up to several days and months ahead." Journal of Hydroinformatics 20, no. 1 (2017): 134–48. http://dx.doi.org/10.2166/hydro.2017.142.

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Abstract In this study, an integrated artificial neural network (IANN) model incorporating both observed and predicted time series as input variables conjoined with wavelet transform for flow forecasting with different lead times. The daily model employs forecasts of the tributaries in its input structure in order to predict the daily flow in the main river in the next time steps. The predictive models for the tributaries are those of the conventional wavelet-ANN models in which they comprised only observed time series as input variables. The monthly model updates its input structure by other

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Krishna, B., Y. R. Satyaji Rao, and P. C. Nayak. "Time Series Modeling of River Flow Using Wavelet Neural Networks." Journal of Water Resource and Protection 03, no. 01 (2011): 50–59. http://dx.doi.org/10.4236/jwarp.2011.31006.

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Yildirim, Haci Ahmet, Avadis Simon Hacinliyan, Ergun Eray Akkaya, and Cercis Ikiel. "Chaos in Time Series of Sakarya River Daily Flow Rate." Journal of Applied Mathematics and Physics 04, no. 10 (2016): 1849–58. http://dx.doi.org/10.4236/jamp.2016.410187.

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Sami, Salman Bin, Sobia Shakeel, and Reema Salman. "Comparison of the hydrological time series modeling by the floods in river Indus of Pakistan." International Journal of Hydrology 6, no. 4 (2022): 130–40. http://dx.doi.org/10.15406/ijh.2022.06.00317.

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Today, in the field of science and technology, huge forecasting applications are used by scholars to forecast future values. Nowadays, using estimating the flood forecasting for peak flow discharges is very common for the risk assessment annually by quantitative data collections from different resources. The very famous and longest rivers of Pakistan i.e. Indus River and other rivers too like River Jhelum, River Kabul, and River Chenab are the prime sources of flooding. These rivers are the prime tributaries of the Indus River System. Pakistan's longest river, River Indus, is connected with th

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ISMAILOVA, I. G., G. KH ISMAIYLOV, L. D. RATKOVICH, N. V. MURASHCHENKOVA, and A. V. PERMINOV. "ANALYSIS AND FORECAST OF RIVER WATERS IN THE VOLGA RIVER RUNOFF FORMATION ZONE BY THE TRENDS METHOD." Prirodoobustrojstvo, no. 2 (2022): 69–78. http://dx.doi.org/10.26897/1997-6011-2022-2-69-78.

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The results of the study on the analysis, generalization and forecast of changes in the average annual and seasonal volumes (flows) of the Volga River to Volgograd (the formation zone) are presented. For the forecast, time flow series are used for the periods 1881/1882-2020/2021, N-140 years and 1914/1915/2020/2021, N-107 years. These periods are accepted as the basic periods characterizing the formation of the Volga River flow to Volgograd. It is revealed that the long-term fluctuations of the Volga River flow contain three periods, the first series, two periods, the second series, signifi ca

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Coloane Luque, Rodrigo A., and Luis A. Gil Alana. "Long memory of river streams in the canal of Panama watershed." International Journal of Hydrology 7, no. 3 (2023): 130–35. http://dx.doi.org/10.15406/ijh.2023.07.00348.

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Study region: It refers to the time series analysis of streamflow patterns in the six principal rivers of the Canal of Panama. Study focus: Using recent time series modeling techniques in fractional or I(d) frameworks, it offer an insight into the hydrology of the Panama Canal watershed. Objectives: The main objective is to determine if the river flows at the Canal of Panama watershed display long memory, and based on that, investigate if significant trends are present across time. Novelty: The use of updated time series techniques in the analysis of long memory and time trends in river flow d

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Yu, Xinying, Shie-Yui Liong, and Vladan Babovic. "EC-SVM approach for real-time hydrologic forecasting." Journal of Hydroinformatics 6, no. 3 (2004): 209–23. http://dx.doi.org/10.2166/hydro.2004.0016.

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This study demonstrates a combined application of chaos theory and support vector machine (SVM) in the analysis of chaotic time series with a very large sample data record. A large data record is often required and causes computational difficulty. The decomposition method is used in this study to circumvent this difficulty. The various parameters inherent in chaos technique and SVM are optimised, with the assistance of an evolutionary algorithm, to yield the minimal prediction error. The performance of the proposed scheme, EC-SVM, is demonstrated on two daily runoff time series: Tryggevælde ca

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Wael, Almikaeel, and Cubanova Lea. "Seasonal decomposition of different types of rivers in Slovakia: Implications for water management and agriculture." MATEC Web of Conferences 385 (2023): 01006. http://dx.doi.org/10.1051/matecconf/202338501006.

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This study conducts an analysis of the seasonal decomposition of various river types in Slovakia in order to enhance the understanding of hydrological time series analysis and its potential implications for water management and agriculture. A comprehensive statistical methodology including decomposition of time series and correlation analysis was applied to the data collected from multiple river gauging stations across Slovakia. The results of the study indicate distinct seasonal variations in the hydrological characteristics of different river types, with a clear differentiation between lowla

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Rutkowska, Agnieszka, and Marek Ptak. "On Certain Stationarity Tests for Hydrologic Series." Studia Geotechnica et Mechanica 34, no. 1 (2012): 51–63. http://dx.doi.org/10.1515/sgem-2017-0022.

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Abstract The main aim of the article was application of some statistical tests for investigation of weak stationarity of hydrologic time series. The tests were applied to mean monthly flow and maximum annual flow on three rivers: two Polish and one American river. Firstly, the modified Mann–Kendall test for autocorrelated data was used to detect trend. After detrending we used “unit root tests” based on the DF test and “stationarity tests” based on the KPSS test. The tests were investigated and compared in some aspects: analysis of residuals, application to seasonal series, AIC and Schwarz val

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21

Melnik, Anton, Ihor Berezka, and Vyacheslav Yavkin. "Estimation of maximum water flow in case of isufficient data of hydrometric observations on the example of the Siret river basin." Scientific Herald of Chernivtsi University. Geography, no. 814 (December 25, 2019): 4–8. https://doi.org/10.31861/geo.2019.814.4-8.

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Catastrophic flood hydrograph components are formed depending on spatial variation and seasoning of meteorological elements. However, from year to year, the role of anthropogenic factors becomes more and more substantial, and the latter significantly effect on the duration of flood passage. The time series of maximal flow rates within natural-economic basin system of the Siret River were considered in the present work, which, with the use of the rivers-analogues, allowed for specification of said rates’ values at 1% provision probability. In comparison to previous publications, too gener

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Rosenthal, O. M., and G. B. Krokhin. "Hydrochemical Dynamics of River Flow in a Mountain River (Katun River as an Example)." Ecology and Industry of Russia 29, no. 1 (2025): 59–65. https://doi.org/10.18412/1816-0395-2025-1-59-65.

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It is noted that the efficiency of protection and rational use of water resources decreases due to uncertainty of information about spontaneously changing indicators of their quality, uniqueness of such variability and, consequently, due to impossibility to develop uniform rules of economic use of water bodies and watercourses. It is shown that these problems arise not only in water bodies subjected to intensive anthropogenic impact, but also in those remaining in practically natural conditions. The results of the authors' studies of the rivers of the Altai Mountains are presented, where such

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Březková, L., M. Starý, and P. Doležal. "The real-time stochastic flow forecast." Soil and Water Research 5, No. 2 (2010): 49–57. http://dx.doi.org/10.17221/13/2009-swr.

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In the Czech Republic, deterministic flow forecasts with the lead time of 48 hours, calculated by rainfall-runoff models for basins of a size of several hundreds to thousands square kilometers, are nowadays a common part of the operational hydrological service. The Czech Hydrometeorological Institute (CHMI) issues daily the discharge forecast for more than one hundred river profiles. However, the causal rainfall is a random process more than a deterministic one, therefore the deterministic discharge forecast based on one precipitation prediction is a significant simplification of the reality.

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HU, T. S., K. C. LAM, and S. T. NG. "River flow time series prediction with a range-dependent neural network." Hydrological Sciences Journal 46, no. 5 (2001): 729–45. http://dx.doi.org/10.1080/02626660109492867.

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Park, Kidoo, Younghun Jung, Kyungtak Kim, and Seung Kook Park. "Determination of Deep Learning Model and Optimum Length of Training Data in the River with Large Fluctuations in Flow Rates." Water 12, no. 12 (2020): 3537. http://dx.doi.org/10.3390/w12123537.

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Recently, developing countries have steadily been pushing for the construction of stream-oriented smart cities, breaking away from the existing old-town-centered development in the past. Due to the accelerating effects of climate change along with such urbanization, it is imperative for urban rivers to establish a flood warning system that can predict the amount of high flow rates of accuracy in engineering, compared to using the existing Computational Fluid Dynamics (CFD) models for disaster prevention. In this study, in the case of streams where missing data existed or only small observation

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Shaofu, Ma, Anas Mahmood Al-Juboori, Asmaa Hussein Alwan, and Abdel-Salam G. Abdel-Salam. "On the Investigation of Monthly River Flow Generation Complexity Using the Applicability of Machine Learning Models." Complexity 2021 (July 7, 2021): 1–14. http://dx.doi.org/10.1155/2021/3721661.

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Streamflow is associated with several sources on nonstationaries and hence developing machine learning (ML) models is always the motive to provide a reliable methodology to understand the actual mechanism of streamflow. The current research was devoted to generating monthly streamflows from annual streamflow. In this study, three different ML models were applied for this purpose, including Multiple Additive Regression Trees (MART), Group Methods of Data Handling (GMDH), and Gene Expression Programming (GEP). The models were developed based on annual streamflow and monthly time index of three r

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Saba, Tanzila. "Neural Approach to Predict Flow Discharge in River Chenab Pakistan." Journal of Advanced Computational Intelligence and Intelligent Informatics 20, no. 5 (2016): 730–34. http://dx.doi.org/10.20965/jaciii.2016.p0730.

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River water flow forecast in general and particularly in floods is of worth importance for monitoring operations of floods in canals and rivers. Floods in rivers bring destructions to road, houses, crops and causes human dislocation. The River Chenab is one of the largest rivers in Pakistan and has a historical recording of heavy floods. Prior to heavy floods, in time warning is mandatory to save lives and property. Accordingly, this paper presents an intelligent model to predict an advance alarming water flow from Chenab River. Standard learning algorithm is applied to train the ANN for this

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Graf, Renata, and Viktor Vyshnevskyi. "Forecasting Monthly River Flows in Ukraine under Different Climatic Conditions." Resources 11, no. 12 (2022): 111. http://dx.doi.org/10.3390/resources11120111.

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River-flow forecasts are important for the management and planning of water resources and their rational use. The present study, based on direct multistep-ahead forecasting with multiple time series specific to the XGBoost algorithm, estimates the long-term changes and forecast monthly flows of selected rivers in Ukraine. In a new, applied approach, a single multioutput model was proposed that forecasts over both short- and long-term horizons using grouped or hierarchical data series. Three forecast stages were considered: using train and test subsets, using a model with train-test data, and t

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Rossi, Daniele, Guido Zolezzi, Walter Bertoldi, and Alfonso Vitti. "Monitoring Braided River-Bed Dynamics at the Sub-Event Time Scale Using Time Series of Sentinel-1 SAR Imagery." Remote Sensing 15, no. 14 (2023): 3622. http://dx.doi.org/10.3390/rs15143622.

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Remote sensing plays a central role in the assessment of environmental phenomena and has increasingly become a powerful tool for monitoring shorelines, river morphology, flood-wave delineation and flood assessment. Optical-based monitoring and the characterization of river evolution at long time scales is a key tool in fluvial geomorphology. However, the evolution occurring during extreme events is crucial for the understanding of the river dynamics under severe flow conditions and requires the processing of data from active sensors to overcome cloud obstructions. This work proposes a cloud-ba

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Siuta, Tomasz. "MODELING OF THE FLOW TIME SERIES FORA SHORT-TERM HYDROLOGICAL FORECAST." Acta Scientiarum Polonorum Formatio Circumiectus 19, no. 3 (2020): 3–14. http://dx.doi.org/10.15576/asp.fc/2020.19.3.3.

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Aim of the study: Within this article an example of an effective approach to real-time, short term forecast of flood rates within Vistula river differential catchment was presented. This forecast is based on flow rates time series measured at the water gauge input and output cross sections of the river system with a daily delay without taking into account any precipitation data. Material and methods: In order to assess the quality of the forecast, four types of time series models were developed for the Smolice outlet gage station. The first type of model is the conventional linear autoregressi

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Di Ciacca, Antoine, Scott Wilson, Jasmine Kang, and Thomas Wöhling. "Deriving transmission losses in ephemeral rivers using satellite imagery and machine learning." Hydrology and Earth System Sciences 27, no. 3 (2023): 703–22. http://dx.doi.org/10.5194/hess-27-703-2023.

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Abstract. Transmission losses are the loss in the flow volume of a river as water moves downstream. These losses provide crucial ecosystem services, particularly in ephemeral and intermittent river systems. Transmission losses can be quantified at many scales using different measurement techniques. One of the most common methods is differential gauging of river flow at two locations. An alternative method for non-perennial rivers is to replace the downstream gauging location by visual assessments of the wetted river length on satellite images. The transmission losses are then calculated as the

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Egop, S.E., and L.W. Arimieari. "Optimum Approximation of Flood Flow in the River Niger Basin." Journal of Advances in Civil Engineering and Management 7, no. 2 (2024): 48–59. https://doi.org/10.5281/zenodo.11065832.

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<em>Providing a trustworthy approximation of flood features using historical time series data for the Lokoja hydrologic station in the Nigerian Niger River Basin was the main goal of this effort. </em><em>The flood statistics were provided by the National Inland Waterways Authority (NIWA) located in Lokoja.</em><em> Using the annual maximum series (AMS), the flood frequency analysis (FFA) approach was used to analyze the extreme data. Five (5) probability distribution models were fitted to the observed data: The Generalized Extreme Value (GEV), Gumbel (EV1), two-parameter log-Normal (LN2), Pea

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Coxon, Gemma, Nans Addor, John P. Bloomfield, et al. "CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain." Earth System Science Data 12, no. 4 (2020): 2459–83. http://dx.doi.org/10.5194/essd-12-2459-2020.

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Abstract. We present the first large-sample catchment hydrology dataset for Great Britain, CAMELS-GB (Catchment Attributes and MEteorology for Large-sample Studies). CAMELS-GB collates river flows, catchment attributes and catchment boundaries from the UK National River Flow Archive together with a suite of new meteorological time series and catchment attributes. These data are provided for 671 catchments that cover a wide range of climatic, hydrological, landscape, and human management characteristics across Great Britain. Daily time series covering 1970–2015 (a period including several hydro

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Cardoso, A. O., and P. L. Silva Dias. "The relationship between ENSO and Paraná River flow." Advances in Geosciences 6 (February 2, 2006): 189–93. http://dx.doi.org/10.5194/adgeo-6-189-2006.

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Abstract. Several studies indicate that there is a relationship between the climatic variability in the South American continent and alterations of the position and intensity of the heat sources in the equatorial region. The El Niño phenomenon can influence the precipitation over some regions of South America such as the Brazilian Northeast, Amazonia, South of Brazil and Uruguay. Over 80% of Brazil's energy comes from hydropower, and decisions concerning future availability and pricing require forecasts of river flow, ideally several months in advance. In this work the relationship between the

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Caren, Marta, and Krešimir Pavlić. "Autocorrelation and cross-correlation flow analysis along the confluence of the Kupa and Sava Rivers." Rudarsko-geološko-naftni zbornik 36, no. 5 (2021): 67–77. http://dx.doi.org/10.17794/rgn.2021.5.7.

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In this paper, an autocorrelation and cross-correlation analysis of the flow of the Kupa and Sava rivers was performed. The analysis was performed at hydrological stations close to the confluence of these two rivers near the city of Sisak, based on data of mean daily flows and daily precipitation. The analysed time period is from 2008 to 2017, with the series being divided into two parts of five years each, from 2008 to 2012 and 2013 to 2017. Daily flow data were measured at the hydrological stations Farkašić on the Kupa River and Crnac on the Sava River, and data on precipitation at the main

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Samoilova, Svetlana Yu, Elena V. Mardasova, and Andrey A. Kolomeytsev. "Changes in hydrological characteristics of the Ob River caused by climate change in 1922–2020 (according to the observations in Barnaul)." Bulletin of the Tomsk Polytechnic University Geo Assets Engineering 336, no. 4 (2025): 75–88. https://doi.org/10.18799/24131830/2025/4/4698.

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Relevance. Violation of hydrological characteristics stationarity caused by climate change and the necessity to take into account the flow change in hydrological studies and prognoses. Aim. To assess the hydrological characteristics evaluation and analyze the Ob River flow directional changes near the city of Barnaul and the flow-forming factors related to the changes (air temperature and precipitations according to the Barnaul meteorological station data). Methods. Complex geographic-hydrological analysis; statistical analysis of time series including the evaluation of the time series homogen

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Seabra-Silva, Joana, Paula Milheiro-Oliveira, and Paulo Avilez-Valente. "Hourly Discharge Modelling and Forecast for a Run-of-river Dam." WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT 21 (March 18, 2025): 137–44. https://doi.org/10.37394/232015.2025.21.12.

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Water resources have become a growing concern in society. This is largely due to the scarcity of this natural asset and the realisation that increasing demand could lead to future conflicts. Sometimes, human action limits access to water or alters natural flows. Run-of-river hydropower schemes manage river flows on a short-term basis, altering the natural flow of rivers according to the energy needs of consumers or the risk of flooding. The aim of this work is to show how to model and predict the hourly flow in a run-of-river reservoir, using the Crestuma-Lever dam on the river Douro (Portugal

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Eriyagama, N., V. Smakhtin, and K. Jinapala. "The Sri Lanka environmental flow calculator: a science-based tool to support sustainable national water management." Water Policy 18, no. 2 (2015): 480–92. http://dx.doi.org/10.2166/wp.2015.158.

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As Sri Lankan water resources are being increasingly exploited, particularly for hydropower and irrigation, ecologists, water practitioners and policymakers alike are realizing the importance of protecting these resources and setting environmental sustainability thresholds. Environmental flows (EF) – the concept that helps define such thresholds – has now become an integral part of environmental impact assessments of river basin development projects. Considering EF is especially vital in the context of the accelerated infrastructure development program, launched after the end of the war in the

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Mondal, M. Shahjahan, and Jahir Uddin Chowdhury. "Generation of 10-day flow of the Brahmaputra River using a time series model." Hydrology Research 44, no. 6 (2012): 1071–83. http://dx.doi.org/10.2166/nh.2012.242.

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Time series models are used in hydrology for the generation of river flow data. The development of such a model, namely deseasonalized Autoregressive Moving Average (ARMA), for the generation of 10-day flows of the Brahmaputra River in Bangladesh is described. The model was fitted following systematic stages of identification, estimation and diagnostic checking of model building. A negative power transformation for the Brahmaputra flow was found to be necessary for model construction. The seasonality of the flow was removed by Fourier analysis using five harmonics for 10-day means and 13 harmo

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Sim, C. H. "A mixed gamma ARMA(1, 1) Model for river flow time series." Water Resources Research 23, no. 1 (1987): 32–36. http://dx.doi.org/10.1029/wr023i001p00032.

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Prakash, Om, K. P. Sudheer, and K. Srinivasan. "Improved higher lead time river flow forecasts using sequential neural network with error updating." Journal of Hydrology and Hydromechanics 62, no. 1 (2014): 60–74. http://dx.doi.org/10.2478/johh-2014-0010.

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Abstract This paper presents a novel framework to use artificial neural network (ANN) for accurate forecasting of river flows at higher lead times. The proposed model, termed as sequential ANN (SANN), is based on the heuristic that a mechanism that provides an accurate representation of physical condition of the basin at the time of forecast, in terms of input information to ANNs at higher lead time, helps improve the forecast accuracy. In SANN, a series of ANNs are connected sequentially to extend the lead time of forecast, each of them taking a forecast value from an immediate preceding netw

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Szolgayová, Elena Peksová, Michaela Danačová, Magda Komorniková, and Ján Szolgay. "Hybrid Forecasting of Daily River Discharges Considering Autoregressive Heteroscedasticity." Slovak Journal of Civil Engineering 25, no. 2 (2017): 39–48. http://dx.doi.org/10.1515/sjce-2017-0011.

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AbstractIt is widely acknowledged that in the hydrological and meteorological communities, there is a continuing need to improve the quality of quantitative rainfall and river flow forecasts. A hybrid (combined deterministic-stochastic) modelling approach is proposed here that combines the advantages offered by modelling the system dynamics with a deterministic model and a deterministic forecasting error series with a data-driven model in parallel. Since the processes to be modelled are generally nonlinear and the model error series may exhibit nonstationarity and heteroscedasticity, GARCH-typ

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Tarpanelli, Angelica, Filippo Iodice, Luca Brocca, Marco Restano, and Jérôme Benveniste. "River Flow Monitoring by Sentinel-3 OLCI and MODIS: Comparison and Combination." Remote Sensing 12, no. 23 (2020): 3867. http://dx.doi.org/10.3390/rs12233867.

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The monitoring of rivers by satellite is an up-to-date subject in hydrological studies as confirmed by the interest of space agencies to finance specific missions that respond to the quantification of surface water flows. We address the problem by using multi-spectral sensors, in the near-infrared (NIR) band, correlating the reflectance ratio between a dry and a wet pixel extracted from a time series of images, the C/M ratio, with five river flow-related variables: water level, river discharge, flow area, mean flow velocity and surface width. The innovative aspect of this study is the use of t

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Panagopoulos, Yiannis, Kostas Stefanidis, Marta Faneca Sanchez, et al. "Pan-European Calculation of Hydrologic Stress Metrics in Rivers: A First Assessment with Potential Connections to Ecological Status." Water 11, no. 4 (2019): 703. http://dx.doi.org/10.3390/w11040703.

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The hydrologic regime of a river is one of the factors determining its ecological status. This paper tries to indicate the present hydrologic stress occurring across European rivers on the basis of model integration. This results in a pan-European assessment at the resolution of the functional elementary catchment (FEC), based on simulated daily time-series of river flows from the model PCR-GLOBWB. To estimate proxies of the present hydrologic stress, two datasets of river flow were simulated under the same climate, one from a hypothetic least disturbed condition scenario and the second from t

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Keskin, M. Erol, Dilek Taylan, and Ecir Ugur Kucuksille. "Data mining process for modeling hydrological time series." Hydrology Research 44, no. 1 (2012): 78–88. http://dx.doi.org/10.2166/nh.2012.003.

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The main purpose of this study was to develop an optimum flow prediction model, based on data mining process. The data mining process was applied to predict river flow of Seyhan Stream in the southern part of Turkey. Hydrological time series modeling was applied using monthly historical flow records to predict Seyhan Stream flows. Seyhan Stream flows were modeled by Markov models and it was seen that it adapted AR(2). Hence, Ft–2 and Ft–1 flows in (t–2) and (t–1) months were the taken inputs. For monthly streamflow predictions, data were taken from the General Directorate of Electrical Power R

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Morey, Steven L., and Dmitry S. Dukhovskoy. "Analysis Methods for Characterizing Salinity Variability from Multivariate Time Series Applied to the Apalachicola Bay Estuary." Journal of Atmospheric and Oceanic Technology 29, no. 4 (2012): 613–28. http://dx.doi.org/10.1175/jtech-d-11-00136.1.

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Abstract Statistical analysis methods are developed to quantify the impacts of multiple forcing variables on the hydrographic variability within an estuary instrumented with an enduring observational system. The methods are applied to characterize the salinity variability within Apalachicola Bay, a shallow multiple-inlet estuary along the northeastern Gulf of Mexico coast. The 13-yr multivariate time series collected by the National Estuary Research Reserve at three locations within the bay are analyzed to determine how the estuary responds to variations in external forcing mechanisms, such as

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Tamagnone, Paolo, Giovanni Massazza, Alessandro Pezzoli, and Maurizio Rosso. "Hydrology of the Sirba River: Updating and Analysis of Discharge Time Series." Water 11, no. 1 (2019): 156. http://dx.doi.org/10.3390/w11010156.

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The Sahelian regions are affected by an increasing number of catastrophic floods in recent years as a consequence of climate and land use/land cover changes. River flow data is key to understanding river behavior and develop flood mitigation and prevention strategies. The present study provides a revision and an update of the existing discharge dataset of the Sirba River with the aim of enhancing the reliability of these data. The revision also includes the recalibration of the Garbey Kourou rating curves. The analysis of the revised discharge time series strengthens the previous findings, evi

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Yuan, Kangqi, Junying Chu, Zuhao Zhou, et al. "Identifying Non-Perennial River Reaches: A Hybrid Model Combining WEP-L and Random Forest." Sustainability 16, no. 23 (2024): 10543. https://doi.org/10.3390/su162310543.

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The proportion of non-perennial rivers within the global river network is increasing, and research on these rivers has significantly grown in recent years due to their important role in water resource management and ecosystems. However, existing identification methods primarily rely on river networks with monitoring data and often overlook the temporal variation in flow, limiting further research and analysis. We propose a novel identification approach that couples the WEP-L model with random forest prediction, based on a comprehensive analysis of the limitations of current methods. Specifical

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Lu, Xiaorong, Xuelei Wang, Liang Zhang, et al. "Improving forecasting accuracy of river flow using gene expression programming based on wavelet decomposition and de-noising." Hydrology Research 49, no. 3 (2017): 711–23. http://dx.doi.org/10.2166/nh.2017.115.

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Abstract Due to the effects of anthropogenic activities and natural climate change, streamflows of rivers have gradually decreased. In order to maintain reliable water supplies, reservoir operation and water resource management, accurate streamflow forecasts are very important. Based on monthly flow data from five hydrological stations in the middle and lower parts of the Hanjiang River Basin, between 1989 and 2009, we consider an efficient approach of adopting the gene expression programming model based on wavelet decomposition and de-noising (WDDGEP) to forecast river flow. Original flow tim

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Bauzha, Tetiana, and Liudmyla Gorbachova. "The Features of the Cyclical Fluctuations, Homogeneity and Stationarity of the Average Annual Flow of the Southern Buh River Basin." Annals of Valahia University of Targoviste, Geographical Series 17, no. 1 (2017): 5–17. http://dx.doi.org/10.1515/avutgs-2017-0001.

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Abstract In the last decades, according to researches of scientists of the world becomes more evident that the global and regional climate changes impact on the water regime of rivers and leads to break of the stationarity of observations series. In this study we analysed the long-term (since the beginning of the observations to 2012) average annual flow of the Southern Buh River Basin (37 gauging stations) and its cyclical fluctuations during a long period of time. The methodological approaches based on the using of hydro-genetic methods for estimation the homogeneity and stationarity of hydr

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