Relevant bibliographies by topics / CA Markov prediction

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'CA Markov prediction'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 February 2022

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Journal articles on the topic "CA Markov prediction"

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Song, Wang, Zhao Yunlin, Xu Zhenggang, Yang Guiyan, Huang Tian, and Ma Nan. "Landscape pattern and economic factors’ effect on prediction accuracy of cellular automata-Markov chain model on county scale." Open Geosciences 12, no. 1 (August 6, 2020): 626–36. http://dx.doi.org/10.1515/geo-2020-0162.

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AbstractUnderstanding and modeling of land use change is of great significance to environmental protection and land use planning. The cellular automata-Markov chain (CA-Markov) model is a powerful tool to predict the change of land use, and the prediction accuracy is limited by many factors. To explore the impact of land use and socio-economic factors on the prediction of CA-Markov model on county scale, this paper uses the CA-Markov model to simulate the land use of Anren County in 2016, based on the land use of 1996 and 2006. Then, the correlation between the land use, socio-economic data and the prediction accuracy was analyzed. The results show that Shannon’s evenness index and population density having an important impact on the accuracy of model predictions, negatively correlate with kappa coefficient. The research not only provides a reference for correct use of the model but also helps us to understand the driving mechanism of landscape changes.
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Kang, Junfeng, Lei Fang, Shuang Li, and Xiangrong Wang. "Parallel Cellular Automata Markov Model for Land Use Change Prediction over MapReduce Framework." ISPRS International Journal of Geo-Information 8, no. 10 (October 13, 2019): 454. http://dx.doi.org/10.3390/ijgi8100454.

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The Cellular Automata Markov model combines the cellular automata (CA) model’s ability to simulate the spatial variation of complex systems and the long-term prediction of the Markov model. In this research, we designed a parallel CA-Markov model based on the MapReduce framework. The model was divided into two main parts: A parallel Markov model based on MapReduce (Cloud-Markov), and comprehensive evaluation method of land-use changes based on cellular automata and MapReduce (Cloud-CELUC). Choosing Hangzhou as the study area and using Landsat remote-sensing images from 2006 and 2013 as the experiment data, we conducted three experiments to evaluate the parallel CA-Markov model on the Hadoop environment. Efficiency evaluations were conducted to compare Cloud-Markov and Cloud-CELUC with different numbers of data. The results showed that the accelerated ratios of Cloud-Markov and Cloud-CELUC were 3.43 and 1.86, respectively, compared with their serial algorithms. The validity test of the prediction algorithm was performed using the parallel CA-Markov model to simulate land-use changes in Hangzhou in 2013 and to analyze the relationship between the simulation results and the interpretation results of the remote-sensing images. The Kappa coefficients of construction land, natural-reserve land, and agricultural land were 0.86, 0.68, and 0.66, respectively, which demonstrates the validity of the parallel model. Hangzhou land-use changes in 2020 were predicted and analyzed. The results show that the central area of construction land is rapidly increasing due to a developed transportation system and is mainly transferred from agricultural land.
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Ildoromi, Alireza, and Mahtab Safari Shad. "Land Use Change Prediction using a Hybrid (CA-Markov) Model." Ecopersia 5, no. 1 (March 1, 2017): 1631–40. http://dx.doi.org/10.18869/modares.ecopersia.5.1.1631.

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Li, Xiuquan, Meizhen Wang, Xuejun Liu, Zhuan Chen, Xiaojian Wei, and Weitao Che. "MCR-Modified CA–Markov Model for the Simulation of Urban Expansion." Sustainability 10, no. 9 (August 31, 2018): 3116. http://dx.doi.org/10.3390/su10093116.

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Ecosystem balance is an important factor that affects healthy and sustainable urban development. The traditional cellular automata (CA) model considers only a few ecological factors, however, the MCR model can account for ecological factors. In previous studies, few ecological factors were added to the CA model. Thus, the minimal cumulative resistance (MCR) model is combined with the CA and Markov models for the simulation of urban expansion. To verify the reliability of the method, the Wuhan metropolitan area was selected as a representative urban area, and its expansion in the past and future was simulated. Firstly, seven influential factors were selected from the perspective of location theory. The transformation rules of the comprehensive resistance surface followed by the modified CA–Markov model were constructed on the basis of the MCR model. The expansion of the Wuhan metropolitan area in 2013 was simulated on the basis of the 1996 and 2006 maps of land-use status, and the kappa coefficient was used as an index to evaluate the accuracy of the proposed method. Then, the expansion of the Wuhan metropolitan area in 2020 was simulated. Finally, the simulation results obtained with and without the MCR model were compared and analysed from the macro- and micro levels. Results show that the prediction accuracy of the two models differed for ecological regions, such as woodlands and water bodies. The similarities between the regions that were overestimated and underestimated by the MCR-modified CA–Markov model and non-MCR model may be attributed to solution of the land-use transfer matrix with the Markov model. The accuracy of the MCR-modified CA–Markov model for predicting forests, water and other ecological regions was higher than that of the Markov model. Therefore, the proposed MCR-modified CA–Markov model has potential applications in environmentally-conscious urban expansion.
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Mondal, M. S., N. Sharma, M. Kappas, and P. K. Garg. "CELLULAR AUTOMATA (CA) CONTIGUITY FILTERS IMPACTS ON CA MARKOV MODELING OF LAND USE LAND COVER CHANGE PREDICTIONS RESULTS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 22, 2020): 1585–91. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-1585-2020.

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Abstract. In this study, attempts has been made to find out cellular automata (CA) contiguity filters impacts on Land use land cover change predictions results. Cellular Automata (CA) Markov chain model used to monitor and predict the future land use land cover pattern scenario in a part of Brahmaputra River Basin, India, using land use land cover map derived from multi-temporal satellite images. Land use land cover maps derived from satellite images of Landsat MSS image of 1987 and Landsat TM image of 1997 were used to predict future land use land cover of 2007 using Cellular Automata Markov model. The validity of the Cellular Automata Markov process for projecting future land use and cover changes calculates using various Kappa Indices of Agreement (Kstandard) predicted (results) maps with the reference map (land use land cover map derived from IRS-P6 LISS III image of 2007). The validation shows Kstandard is 0.7928. 3x3, 5x5 and 7x7 CA contiguity filters are evaluated to predict LULC in 2007 using 1987 and 1997 LULC maps. Regression analysis have been carried out for both predicted quantity as well as prediction location to established the cellular automata (CA) contiguity filters impacts on predictions results. Correlation established that predicted LULC of 2007 and LULC derived from LISS III Image of 2007 are strongly correlated and they are slightly different to each-other but the quantitative prediction results are same for when 3x3, 5x5 and 7x7 CA contiguity filters are evaluated to predict land use land cover. When we look at the quantity of predicted land use land cover of 2007 area statistics are derived by using 3x3, 5x5 and 7x7 CA contiguity filters, the predicted area statistics are the same. Other hands, the spatial difference between predicted LULC of 2007 and LULC derived from LISS III images of 2007 is evaluated and they are found to be slightly different. Correlation coefficient (r) between predicted LULC classes and LULC derived from LISS III image of 2007 using 3x3, 5x5, 7x7 are 0.7906, 0.7929, 0.7927, respectively. Therefore, the correlation coefficient (r) for 5x5 contiguity filters is highest among 3x3, 5x5, and 7x7 filters and established/produced most geographically / spatially distributed effective results, although the differences between them are very small.
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ایلدرمی, علیرضا, حمید نوری, مهین نادری, سهیلا آقابیگی امین, and حسین زینی وند. "Land use change prediction using Markov chain and CA Markov Model (Case Study: Gareen Watershed)." journal of watershed management research 8, no. 16 (February 1, 2018): 232–40. http://dx.doi.org/10.29252/jwmr.8.16.232.

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Park, Geun-Ae, and Seong-Joon Kim. "Prediction of the Urbanization Progress Using Factor Analysis and CA-Markov Technique." Journal of The Korean Society of Agricultural Engineers 49, no. 6 (November 30, 2007): 105–14. http://dx.doi.org/10.5389/ksae.2007.49.6.105.

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Ge, Yuan, Yan Zhang, Wengen Gao, Fanyong Cheng, Nuo Yu, and Jincenzi Wu. "Modelling and Prediction of Random Delays in NCSs Using Double-Chain HMMs." Discrete Dynamics in Nature and Society 2020 (October 29, 2020): 1–16. http://dx.doi.org/10.1155/2020/6848420.

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This paper is concerned with the modelling and prediction of random delays in networked control systems. The stochastic distribution of the random delay in the current sampling period is assumed to be affected by the network state in the current sampling period as well as the random delay in the previous sampling period. Based on this assumption, the double-chain hidden Markov model (DCHMM) is proposed in this paper to model the delays. There are two Markov chains in this model. One is the hidden Markov chain which consists of the network states and the other is the observable Markov chain which consists of the delays. Moreover, the delays are also affected by the hidden network states, which constructs the DCHMM-based delay model. The initialization and optimization problems of the model parameters are solved by using the segmental K-mean clustering algorithm and the expectation maximization algorithm, respectively. Based on the model, the prediction of the controller-to-actuator (CA) delay in the current sampling period is obtained. The prediction can be used to design a controller to compensate the CA delay in the future research. Some comparative experiments are carried out to demonstrate the effectiveness and superiority of the proposed method.
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Liu, Pei, Shoujun Jia, Ruimei Han, and Hanwei Zhang. "Landscape Pattern and Ecological Security Assessment and Prediction Using Remote Sensing Approach." Journal of Sensors 2018 (June 4, 2018): 1–14. http://dx.doi.org/10.1155/2018/1058513.

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In this work, we present a processing chain for landscape pattern and ecological security status assessment and prediction based on cellular automata Markov (CA-Markov) and pressure status response pattern (PSRP) models using remotely sensed data (RSD) captured in 1986, 1996, 2006, 2016, and RSD simulated in 2026 over Zhengzhou city, Henan province, China. Three major findings can be withdrawn through the experiments. First, there is a significant changing of landscape type area, especially for building land. The area of building land is up to more than 5%, from 1986 to 2016. Secondly, the heterogeneity of landscape is increasing, and the diversity of landscape is becoming more and more diversifying and complex. Third, the changing trend of ecological security of Zhengzhou city shaped as decreasing and increasing gradually during the last 40 years. While the ecological security status, nowadays, appeared to a good trend by contrast of the previous stages. The predicted results with CA-Markov model show that the level of ecological security is still in moderate and has a trend of moving toward to the center in 2026.
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Babaeian Diva, A., B. Bigdeli, and P. Pahlavani. "AGRICULTURAL LAND CHANGE DETECTING AND FORECASTING USING COMBINATION OF FEEDFORWARD MULTILAYER NEURAL NETWORK, CELLULAR AUTOMATA AND MARKOV CHAIN MODELS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 153–58. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-153-2019.

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Abstract. This paper proposed a methodology for finding changes in agricultural land of Tehran during past years and simulating these changes for future years. The proposed method utilized the spatial GIS-based techniques and Landsat satellite imagery to predict agricultural land map for the future of Tehran. Therefore, a method for finding and predicting changes based on combining the feedforward multilayer perceptron neural network (MLP), cellular automata (CA), and Markov chain model were applied. In this regard, the Landsat images of 2002, 2008, and 2014 were classified by a binary support vector machine classifier into two classes of agricultural and non-agricultural. Then, the potential transition maps were generated by the neural network MLP and extensible areas were obtained by the Markov chain model. Finally, the results of these two steps were combined with the MOLA method and the 2020 and 2025 agricultural maps were predicted. The proposed method obtained the Kappa factor of 89.92% that indicates the high ability of the neural network and the CA–Markov for finding the changes and prediction in the city of Tehran.
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Dissertations / Theses on the topic "CA Markov prediction"

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Wang, Guiwei. "Automatic information extraction and prediction of karst rocky desertification in Puding using remote sensing data." Thesis, Högskolan i Gävle, Samhällsbyggnad, GIS, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-23988.

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Karst rocky desertification (KRD) is one kind of severe environmental problem existing in southwest of China. Reveal KRD condition is vital to solve the problem. A way to address the problem is by identifying KRD areas, so that policy-makers and researchers may get a better view of the issue and know where the areas affected by the problem are located. The study area is called Puding which is a county located in the central part of Guizhou province. Based on Landsat data, by using GIS and RS techniques, KRD information of Puding was extracted. Furthermore, the study monitored decades of change of the environmental problem in Puding and predicted possible condition in the future. Other researchers and decision makers may get a better view of the issue from the study results. In addition to Landsat data, other used data includes: ASTER Global digital elevation model data, Modis data, Google Earth data and other thematic maps. In the study, expert classification system and spectral features based model two methods were applied to extract KRD information and compare with each other. Their classified rules were taken from previous studies separately. Necessary preprocessing procedures such as atmospheric correction and geometrical correction were performed before extraction. After extraction relevant results were evaluated and analyzed. Predictions were made by cellular automata Markov module. Based on extracted KRD results, the distribution, percentage, change, and prediction of KRD conditions in Puding were presented. The results of the accuracy evaluation showed that the spectral features based model had acceptable performance. However, the KRD results extracted by expert classification system method were poor. The extracted KRD results, including KRD maps and the prediction map, both indicated that KRD areas in Puding were decreased from 1993 (spring) to 2016 (spring) and suggested to pay more attention to KRD areas changes with the seasons
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Nguyen, Thi Thanh Huong, and Thi Thuy Phuong Ngo. "Land use/land cover change prediction in Dak Nong Province based on remote sensing and Markov Chain Model and Cellular Automata." Technische Universität Dresden, 2018. https://tud.qucosa.de/id/qucosa%3A33069.

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Land use and land cover changes (LULCC) including deforestation for agricultural land and others are elements that contribute on global environmental change. Therefore understanding a trend of these changes in the past, current, and future is important for making proper decisions to develop in a sustainable way. This study analyzed land use and land cover (LULC) changes over time for Tuy Duc district belonging to Dak Nong province based on LULC maps classified from a set of multidate satellite images captured in year 2003, 2006, 2009, and 2013 (SPOT 5 satellite images). The LULC spatio-temporal changes in the area were classified as perennial agriculture, cropland, residential area, grassland, natural forest, plantation and water surface. Based on these changes over time, potential LULC in 2023 was predicted using Cellular Automata (CA)–Markov model. The predicted results of the change in LULC in 2023 reveal that the total area of forest will lose 9,031ha accounting of 50% in total area of the changes. This may be mainly caused by converting forest cover to agriculture (account for 28%), grassland (12%) and residential area (9%). The findings suggest that the forest conversion needs to be controlled and well managed, and a reasonable land use plan should be developed in a harmonization way with forest resources conservation.
Thay đổi sử dụng đất và thảm phủ (LULCC) bao gồm cả việc phá rừng để phát triển nông nghiệp và vì các mục đích khác là tác nhân đóng góp vào biến đổi môi trường toàn cầu. Vì vậy hiểu biết về khuynh hướng của sự thay đổi này trong quá khứ, hiện tại và tương lai là quan trọng để đưa ra những quyết định dúng đắn để phát triển bền vững. Nghiên cứu đã phân tích LULCC trong thời gian qua dựa vào các bản đồ sử dụng đất và thảm phủ (LULC) đã được phân loại từ một loạt ảnh vệ tinh đa phổ được thu chụp vào năm 2003, 2006, 2009 (ảnh SPOT 5). Những thay đổi LULC theo thời gian và không gian trong khu vực được phân loại thành đất nông nghiệp với cây dài ngày, cây ngắn ngày, thổ cư, trảng cỏ cây bụi, rừng tự nhiên, rừng trồng và mặt nước. Dựa trên sự thay đổi này theo thời gian, LULC tiềm năng cho năm 2023 đã được dự báo bằng cách sử dụng mô hình CAMarkov. Kết quả dự báo LULCC năm 2023 đã cho thấy tổng diện tích rừng bị mất khoảng 9,031 ha chiếm 50% trong tổng số diện tích thay đổi. Điều này chủ yếu là do chuyển đổi từ rừng tự nhiên sang canh tác nông nghiệp (chiếm 28%), trảng cỏ cây bụi (12%) và khu dân cư (9%). Kết quả cho thấy việc chuyển đổi rừng cần phải được kiểm soát và quản lý tốt và một kế hoạch sử dụng đất hợp lý cần được xây dựng trong sự hài hòa với bảo tồn tài nguyên rừng.
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Book chapters on the topic "CA Markov prediction"

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Halder, Sarmistha, Kaberi Samanta, and Sandipan Das. "Monitoring and Prediction of Dynamics in Sundarban Forest using CA–Markov Chain Model." In Spatial Modeling in Forest Resources Management, 425–38. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56542-8_18.

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Waiyasusri, Katawut, Nayot Kulpanich, Morakot Worachairungreung, and Pornperm Sae-ngow. "Monitor the Land Use Change and Prediction Using CA-Markov Model in Li Pe Island, Satun Province, Thailand." In Springer Geography, 46–58. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33900-5_5.

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Faria de Deus, Raquel, José António Tenedório, and Jorge Rocha. "Modelling Land-Use and Land-Cover Changes." In Methods and Applications of Geospatial Technology in Sustainable Urbanism, 57–102. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-2249-3.ch003.

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In this chapter, a hybrid approach integrating cellular automata (CA), fuzzy logic, logistic regression, and Markov chains for modelling and prediction of land-use and land-cover (LULC) change at the local scale, using geographic information with fine spatial resolution is presented. A spatial logistic regression model was applied to determine the transition rules that were used by a conventional CA model. The overall dimension of LULC change was estimated using a Markov chain model. The proposed CA-based model (termed CAMLucc) in combination with physical variables and land-use planning data was applied to simulate LULC change in Portimão, Portugal between 1947 and 2010 and to predict its future spatial patterns for 2020 and 2025. The main results of this research show that Portimão has been facing massive growth in artificial surfaces, particularly near the main urban settlements and along the coastal area, and reveal an early and intensive urban sprawl over time.
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Begum, Masuma, Niloy Pramanick, Anirban Mukhopadhyay, and Sayani Datta Majumdar. "Scenarios of the Tropical Dry Forest of Purulia District West Bengal." In Practice, Progress, and Proficiency in Sustainability, 254–67. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0014-9.ch013.

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In this chapter, satellite images of the years 1995, 2005, and 2015 of LANDSAT have been used. After pre-processing (geometric correction and atmospheric correction using FLAASH, LULC change dynamics have been assessed to estimate the changes in total forest cover in Purulia district through an unsupervised K-means classification scheme. To evaluate the health status, vegetation indices, namely NDVI, SAVI, and CVI, have been used. The increase in NDVI, SAVI, and CVI values was inferred as no significant degradation of Purulia forest cover. Moreover, future scenarios have been predicted by implementing a CA-MARKOV model. Using the land cover map of 1995 as the base map, and from 1995 to 2005 as training data, a land cover map of 2015 has been generated which in turn validated by the actual land cover of 2015. After validation, prediction of land cover was possible for the years 2035 and 2050. The prediction suggested that forest area will increase by approximately 4% from 2015 to 2035 and by 3% from 2035 to 2050.
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Nehzak, Hassan Khavarian, Maryam Aghaei, Raoof Mostafazadeh, and Hamidreza Rabiei-Dastjerdi. "Evaluation of land use change predictions using CA-Markov model and management scenarios." In Computers in Earth and Environmental Sciences, 105–15. Elsevier, 2022. http://dx.doi.org/10.1016/b978-0-323-89861-4.00017-8.

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Conference papers on the topic "CA Markov prediction"

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Gai, Aihong, Liping Di, Junmei Tang, Liying Guo, Yonglan Qian, Dongmei Zhou, Qian Lu, Jinrui Song, and Guozhang Cen. "Landscape Pattern Analysis and Prediction in Qingyang City Based on CA-Markov Model." In 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics). IEEE, 2018. http://dx.doi.org/10.1109/agro-geoinformatics.2018.8475979.

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Yong Jun Lee, Min Ji Park, Geun Ae Park, and Seong Joon Kim. "A Modified CA-Markov Technique for The Prediction of Future Land Use Change." In 2008 Providence, Rhode Island, June 29 - July 2, 2008. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2008. http://dx.doi.org/10.13031/2013.25082.

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Virtriana, Riantini, Irawan Sumarto, Albertus Deliar, Udjianna S. Pasaribu, and Moh Taufik. "Model of land cover change prediction in West Java using cellular automata-Markov chain (CA-MC)." In NATIONAL PHYSICS CONFERENCE 2014 (PERFIK 2014). AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4915060.

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Vandansambuu, Battsengel, Tsolmon Davaa, Byambakhuu Gantumur, Myagmartseren Purevtseren, Otgonbayar Lkhagva, and Falin Wu. "Spatiotemporal monitoring and prediction of land use/land cover changes using CA-Markov chain model: a case study in Orkhon Province, Mongolia." In Remote Sensing Technologies and Applications in Urban Environments V, edited by Nektarios Chrysoulakis, Thilo Erbertseder, and Ying Zhang. SPIE, 2020. http://dx.doi.org/10.1117/12.2574032.

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Virtriana, Riantini, Irawan Sumarto, Albertus Deliar, Agung Budi Harto, Moh Taufik, and Udjianna S. Pasaribu. "The integration method of cellular automata(CA) J-Markov chain(MC), West Java's Northern part characteristics for land cover change prediction study." In 2014 IEEE 2nd International Conference on Technology, Informatics, Management, Engineering & Environment (TIME-E). IEEE, 2014. http://dx.doi.org/10.1109/time-e.2014.7011596.

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Mishra, Vikash Kumar, and Triloki Pant. "Mapping and Prediction of Urban Area with Markov-CA Model using Landsat-8 Images for Effective Management of Urban Area in Prayagraj City." In 2020 URSI Regional Conference on Radio Science ( URSI-RCRS). IEEE, 2020. http://dx.doi.org/10.23919/ursircrs49211.2020.9113566.

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Nishizuka, Satoshi S., Fumitaka Endo, Kazushige Ishida, Hirokatsu Katagiri, Kei Sato, Kohei Kume, Kaoru Ishida, Takeshi Iwaya, Keisuke Koeda, and Go Wakabayashi. "Abstract 5596: Identification of relapse prediction marker for advanced gastric cancer using reverse-phase protein arrays." In Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA. American Association for Cancer Research, 2014. http://dx.doi.org/10.1158/1538-7445.am2014-5596.

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Yamazaki, Nobuyoshi, Yoshikatsu Koga, Norio Saito, and Yasuhiro Matsumura. "Abstract 1491: Tissue miRNA as a predictive marker for recurrence of Dukes B colorectal cancer." In Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA. American Association for Cancer Research, 2014. http://dx.doi.org/10.1158/1538-7445.am2014-1491.

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Moon, Sung Ung, Jin Won Kim, Ji Hea Sung, Mi Hyun Kang, Hyun Chang, Jeong Ok Lee, Yu Jung Kim, et al. "Abstract 912: P21-activated kinase 4 (PAK4) as a predictive marker for gemcitabine in pancreatic cancer cell line." In Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA. American Association for Cancer Research, 2014. http://dx.doi.org/10.1158/1538-7445.am2014-912.

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White, David, Alison Mason, and Ryan Clark. "Long Term Performance of the SkyTrough Solar Concentrator." In ASME 2012 6th International Conference on Energy Sustainability collocated with the ASME 2012 10th International Conference on Fuel Cell Science, Engineering and Technology. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/es2012-91458.

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The SkyTrough is an advanced integrated parabolic trough concentrator designed for high performance and low cost to achieve economic objectives in the market for high grade heat for industrial processes and electrical generation. To achieve low cost, a comprehensive optimization process was carried out for every component based on the choice of low cost silvered polymer film as the reflector. To verify high performance, the optical efficiency of a single module was measured at the National Renewable Energy Lab (NREL), and a demonstration loop was constructed in December, 2009 at the SEGS-II solar power plant in Daggett, CA, USA. This paper compares operating data recorded over eighteen months for the commercial demonstration at the SEGS-II plant with model predictions based on the NREL efficiency measurement. The comparison demonstrates that the SkyTrough system will perform predictably over time. Additional data illustrating the good performance of the collector in wind, and the sustained reflectance of the mirror film, are presented.
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