Relevant bibliographies by topics / Reservoir inflow forecasting

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Academic literature on the topic 'Reservoir inflow forecasting'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 February 2022

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Journal articles on the topic "Reservoir inflow forecasting"

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Uang-Aree, Prawit, and Sununtha Kingpaiboon. "Possibility of GPS precipitable water vapour for reservoir inflow forecasting." Journal of Water and Land Development 36, no. 1 (2018): 161–71. http://dx.doi.org/10.2478/jwld-2018-0016.

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AbstractWe investigated the possibility of using GPS precipitable water vapour (GPS-PWV) for forecasting reservoir inflow. The correlations between monthly GPS-PWV and the inflow of two reservoirs were examined and the relationship tested, using a group method of data handling (GMDH) type neural network algorithm. The daily and monthly reservoir inflows were directly proportional to daily and monthly GPS-PWV trends. Peak reservoir inflow, however, shifted from the peak averages for GPS-PWV. A strong relationship between GPS-PWV and inflow was confirmed by high R2 values, high coefficients of c
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Chiamsathit, Chuthamat, Adebayo J. Adeloye, and Soundharajan Bankaru-Swamy. "Inflow forecasting using Artificial Neural Networks for reservoir operation." Proceedings of the International Association of Hydrological Sciences 373 (May 12, 2016): 209–14. http://dx.doi.org/10.5194/piahs-373-209-2016.

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Abstract. In this study, multi-layer perceptron (MLP) artificial neural networks have been applied to forecast one-month-ahead inflow for the Ubonratana reservoir, Thailand. To assess how well the forecast inflows have performed in the operation of the reservoir, simulations were carried out guided by the systems rule curves. As basis of comparison, four inflow situations were considered: (1) inflow known and assumed to be the historic (Type A); (2) inflow known and assumed to be the forecast (Type F); (3) inflow known and assumed to be the historic mean for month (Type M); and (4) inflow is u
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Yang, Sheng-Chi, and Tsun-Hua Yang. "Uncertainty Assessment: Reservoir Inflow Forecasting with Ensemble Precipitation Forecasts and HEC-HMS." Advances in Meteorology 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/581756.

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During an extreme event, having accurate inflow forecasting with enough lead time helps reservoir operators decrease the impact of floods downstream. Furthermore, being able to efficiently operate reservoirs could help maximize flood protection while saving water for drier times of the year. This study combines ensemble quantitative precipitation forecasts and a hydrological model to provide a 3-day reservoir inflow in the Shihmen Reservoir, Taiwan. A total of six historical typhoons were used for model calibration, validation, and application. An understanding of cascaded uncertainties from t
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Zhong, Yixuan, Shenglian Guo, Huanhuan Ba, Feng Xiong, Fi-John Chang, and Kairong Lin. "Evaluation of the BMA probabilistic inflow forecasts using TIGGE numeric precipitation predictions based on artificial neural network." Hydrology Research 49, no. 5 (2018): 1417–33. http://dx.doi.org/10.2166/nh.2018.177.

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Abstract Reservoir inflow forecasting is a crucial task for reservoir management. Without considering precipitation predictions, the lead time for inflow is subject to the concentration time of precipitation in the basin. With the development of numeric weather prediction (NWP) techniques, it is possible to forecast inflows with long lead times. Since larger uncertainty usually occurs during the forecasting process, much attention has been paid to probabilistic forecasts, which uses a probabilistic distribution function instead of a deterministic value to predict the future status. In this stu
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Ngamsanroaj, Yaowalak, and Kreangsak Tamee. "Improving model using estimate error for daily inflow forecasting." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 13, no. 2 (2020): 170–77. http://dx.doi.org/10.37936/ecti-cit.2019132.198508.

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Inflow forecasting is one of the important components for reservoir operation and resource management. To obtain enhanced accuracy for forecasting reservoir inflow, this paper proposed an improved model for forecasting the inflow of Bhumibol reservoir. The 3,169 records of daily inflow data from June 1, 2008, to February 1, 2017, had been collected to calculate the inflow into the reservoir by using Artificial Neural Networks (ANN) Back-Propagation Learning Algorithm for forecasting the inflow of the reservoir in the main model and error prediction model. The performance of the model can be ev
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Xu, Shichao, Yangbo Chen, Lixue Xing, and Chuan Li. "Baipenzhu Reservoir Inflow Flood Forecasting Based on a Distributed Hydrological Model." Water 13, no. 3 (2021): 272. http://dx.doi.org/10.3390/w13030272.

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For reservoir basins, complex underlying surface conditions, short flood confluence times, and concentrated water volumes make inflow flood forecasting difficult and cause forecast accuracies to be low. Conventional flood forecasting models can no longer meet the required forecast accuracy values for flood control operations. To give full play to the role of reservoirs in flood control and to maximize the use of reservoir flood resources, high-precision inflow flood forecasting is urgently needed as a support mechanism. In this study, the Baipenzhu Reservoir in Guangdong Province was selected
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Kang, Jaewon. "Probabilistic Forecasting of Seasonal Inflow to Reservoir." Journal of the Environmental Sciences international 22, no. 8 (2013): 965–77. http://dx.doi.org/10.5322/jesi.2013.22.8.965.

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Kale, M. U., M. B. Nagdeve, and S. B. Wadatkar. "Reservoir Inflow Forecasting using Artificial Neural Network." Hydrology Journal 35, no. 1and2 (2012): 52. http://dx.doi.org/10.5958/j.0971-569x.35.1x.005.

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Banihabib, Mohammad Ebrahim, Reihaneh Bandari, and Mohammad Valipour. "Improving Daily Peak Flow Forecasts Using Hybrid Fourier-Series Autoregressive Integrated Moving Average and Recurrent Artificial Neural Network Models." AI 1, no. 2 (2020): 263–75. http://dx.doi.org/10.3390/ai1020017.

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In multi-purpose reservoirs, to achieve optimal operation, sophisticated models are required to forecast reservoir inflow in both short- and long-horizon times with an acceptable accuracy, particularly for peak flows. In this study, an auto-regressive hybrid model is proposed for long-horizon forecasting of daily reservoir inflow. The model is examined for a one-year horizon forecasting of high-oscillated daily flow time series. First, a Fourier-Series Filtered Autoregressive Integrated Moving Average (FSF-ARIMA) model is applied to forecast linear behavior of daily flow time series. Second, a
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Lee, Donghee, Hwansuk Kim, Ilwon Jung, and Jaeyoung Yoon. "Monthly Reservoir Inflow Forecasting for Dry Period Using Teleconnection Indices: A Statistical Ensemble Approach." Applied Sciences 10, no. 10 (2020): 3470. http://dx.doi.org/10.3390/app10103470.

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Reliable long-range reservoir inflow forecast is essential to successfully manage water supply from reservoirs. This study aims to develop statistical reservoir inflow forecast models for a reservoir watershed, based on hydroclimatic teleconnection between monthly reservoir inflow and climatic variables. Predictability of such a direct relationship has not been assessed yet at the monthly time scale using the statistical ensemble approach that employs multiple data-driven models as an ensemble. For this purpose, three popular data-driven models, namely multiple linear regression (MLR), support
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Dissertations / Theses on the topic "Reservoir inflow forecasting"

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Burton, Holly. "Reservoir inflow forecasting using time series and neural network models." Thesis, McGill University, 1998. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=29800.

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In practice, the reservoir net inflow is computed based upon the application of the water balance equation to the reservoir system since it is difficult to obtain direct and reliable measurements of this variable. The net inflow process has been thus found to possess a random behaviour because it is related to the stochastic nature of various physical processes involved in the water balance computation (e.g., precipitation, evaporation, etc.). Therefore, the aim of this research is to propose a forecasting method that can accurately and efficiently predict the random reservoir inflow series. T
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Burton, Holly. "Reservoir inflow forecasting using time series and neural network models." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0017/MQ54220.pdf.

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Barnard, Joanna Mary. "The value of inflow forecasting in the operation of a hydroelectric reservoir." Thesis, University of British Columbia, 1989. http://hdl.handle.net/2429/27759.

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The present study examines the value of conceptual hydrologic forecasting in the operation of a hydroelectric generating project. The conceptual forecasting method used is the UBC Watershed Model. The value of the conceptual forecast is determined by comparing results obtained by use of the forecast to those obtained by use of a forecast based purely on the historic record. The effect of the size of the reservoir on the value of the forecast is also considered. The operation of a hypothetical project is modelled using dynamic programming. The operation of the project is optimized using the co
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Zhou, Dequan. "The value of one month ahead inflow forecasting in the operation of a hydroelectric reservoir." Thesis, University of British Columbia, 1991. http://hdl.handle.net/2429/30145.

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The research assesses the value of forecast information in operating a hydro-electric project with a storage reservoir. The benefits are the increased hydro power production, when forecasts are available. The value of short term forecasts is determined by comparing results obtained with the use of one month ahead perfect predictions to those obtained without forecasts but a knowledge of the statistics of the possible flows. The benefits with perfect forecasts provide an upper limit to the benefits which could be obtained with actual less than perfect forecasts. The effects of generating capac
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Sázel, Jiří. "Střednědobé předpovědi průtoků vody v měrném profilu toku." Doctoral thesis, Vysoké učení technické v Brně. Fakulta stavební, 2015. http://www.nusl.cz/ntk/nusl-234548.

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Thesis is aimed on creation of prediction model for releasing medium-term water stream flow forecasts. Created model create forecasts based on principal of finding most similar historical case. Usefulness of forecasting model is demonstrated for operation of one isolated reservoir in gauge profile Oslavany on river Oslava.
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Xie, Ming 1973. "Prediction of daily net inflows for management of reservoir systems." Thesis, McGill University, 2001. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=33043.

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Operational planning of water resource systems like reservoirs and hydropower plants calls for real-time forecasting of reservoir inflow. Reservoir daily inflow forecasts provide a warning of impending floods or drought conditions and help to optimize operating policies for reservoir management based on a fine time scale. The aim of this study was to determine the best model for daily reservoir inflow prediction through linear regression, exponential smoothing and artificial neural network (ANN) techniques. The Hedi reservoir, the third largest reservoir in south China with a 1.144 x 109 m 3,
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Dixon, Samuel G. "Seasonal forecasting of reservoir inflows in data sparse regions." Thesis, Loughborough University, 2017. https://dspace.lboro.ac.uk/2134/33524.

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Management of large, transboundary river systems can be politically and strategically problematic. Accurate flow forecasting based on public domain data offers the potential for improved resource allocation and infrastructure management. This study investigates the scope for reservoir inflow forecasting in data sparse regions using public domain information. Four strategically important headwater reservoirs in Central Asia are used to pilot forecasting methodologies (Toktogul, Andijan and Kayrakkum in Kyrgyzstan and Nurek in Tajikistan). Two approaches are developed. First, statistical forecas
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Bourdin, Dominique R. "A probabilistic inflow forecasting system for operation of hydroelectric reservoirs in complex terrain." Thesis, University of British Columbia, 2013. http://hdl.handle.net/2429/45173.

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This dissertation presents a reliable probabilistic forecasting system designed to predict inflows to hydroelectric reservoirs. Forecasts are derived from a Member-to-Member (M2M) ensemble in which an ensemble of distributed hydrologic models is driven by the gridded output of an ensemble of numerical weather prediction (NWP) models. Multiple parameter sets for each hydrologic model are optimized using objective functions that favour different aspects of forecast performance. On each forecast day, initial conditions for each differently-optimized hydrologic model are updated using meteorologic
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Westra, Seth Pieter Civil &amp Environmental Engineering Faculty of Engineering UNSW. "Probabilistic forecasting of multivariate seasonal reservoir inflows: accounting for spatial and temporal variability." Awarded by:University of New South Wales. Civil & Environmental Engineering, 2007. http://handle.unsw.edu.au/1959.4/40630.

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Hydrological variables such as rainfall and streamfiow vary at a range of temporal scales, from short term (diurnal and seasonal) to the inter annual time scales associated with the El Nino - Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) phenomena, to even longer time scales such as those linked to the Pacific (inter-) Decadal Oscillation (PDO). This temporal variability poses a significant challenge to hydrologists and water resource managers, since a failure to take such variability into account can lead to an underestimation of the likelihood of droughts and sequences of above a
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Signoriello, Giuseppe Alessandro 1977. "Modelos matemáticos para previsão de vazões afluentes à aproveitamentos hidrelétricos." [s.n.], 2014. http://repositorio.unicamp.br/jspui/handle/REPOSIP/265912.

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Orientador: Ieda Geriberto Hidalgo<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica<br>Made available in DSpace on 2018-08-25T19:15:52Z (GMT). No. of bitstreams: 1 Signoriello_GiuseppeAlessandro_M.pdf: 31629174 bytes, checksum: 1674c1adcccf93d9b3ee9711be3f709e (MD5) Previous issue date: 2014<br>Resumo: Este trabalho apresenta a comparação de dois modelos matemáticos desenvolvidos para prever vazões afluentes à usinas hidrelétricas. O objetivo é abordar os aspectos que determinam a qualidade do insumo fundamental para a programação da operação do
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Books on the topic "Reservoir inflow forecasting"

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Vaccaro, J. J. Development, testing, and assessment of regression equations for experimental forecasts of fall-transition-season inflows to the Howard A. Hanson Reservoir, Green River, Washington. U.S. Dept. of the Interior, U.S. Geological Survey, 2000.

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Book chapters on the topic "Reservoir inflow forecasting"

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Tiwari, Mukesh Kumar, and Sanjeet Kumar. "Reservoir Inflow Forecasting Using Extreme Learning Machines." In Hydrologic Modeling. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-5801-1_40.

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Basri, Hidayah, Lariyah Mohd Sidek, A. Z. Abdul Razad, S. R. Mohd Salleh, M. S. Kamarulzaman, and P. Pokhrel. "Performance of Operational Inflow Forecasting System for Hydropower Reservoir." In Water Resources Development and Management. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1971-0_14.

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Oyebode, Oluwaseun, and Josiah Adeyemo. "Reservoir Inflow Forecasting Using Differential Evolution Trained Neural Networks." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07494-8_21.

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Chang, F. J., Y. C. Lo, P. A. Chen, L. C. Chang, and M. C. Shieh. "Multi-Step-Ahead Reservoir Inflow Forecasting by Artificial Intelligence Techniques." In Knowledge-Based Information Systems in Practice. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13545-8_14.

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Weekaew, Jakkarin, Pakorn Ditthakit, and Nichnan Kittiphattanabawon. "Reservoir Inflow Time Series Forecasting Using Regression Model with Climate Indices." In Lecture Notes in Networks and Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79757-7_13.

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Suriya, S., K. Saran, L. Chris Anto, C. Anbalagan, and K. R. Vinodh. "Inflow Forecasting of Bhavanisagar Reservoir Using Artificial Neural Network (ANN): A Case Study." In Lecture Notes in Civil Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5101-7_12.

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Amenu, G. G., and Å. Killingtveit. "Real-time inflow forecasting for GilgelGibe reservoir, Ethiopia." In Hydropower in the New Millennium. CRC Press, 2020. http://dx.doi.org/10.1201/9781003078722-7.

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Conference papers on the topic "Reservoir inflow forecasting"

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Sauhats, Antans, Roman Petrichenko, Zane Broka, Karlis Baltputnis, and Dmitrijs Sobolevskis. "ANN-based forecasting of hydropower reservoir inflow." In 2016 57th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON). IEEE, 2016. http://dx.doi.org/10.1109/rtucon.2016.7763129.

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Huamani, Ivette Raymunda Luna, Rosangela Ballini, Ieda Geriberto Hidalgo, Paulo Sergio Franco Barbosa, and Alberto Luiz Francato. "Daily reservoir inflow forecasting using fuzzy inference systems." In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2011. http://dx.doi.org/10.1109/fuzzy.2011.6007690.

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Knoppová, Kateřina, Daniel Marton, and Petr Štěpánek. "APPLICATION OF RAINFALL-RUNOFF MODEL: CLIMATE CHANGE IMPACTS ON RESERVOIR INFLOW." In XXVII Conference of the Danubian Countries on Hydrological Forecasting and Hydrological Bases of Water Management. Nika-Tsentr, 2020. http://dx.doi.org/10.15407/uhmi.conference.01.11.

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The impacts of climate change are beginning to be felt in the Czech Republic. In recent years, we were challenging a dry period, which threatens to continue affecting Czech economy, agriculture and personal comfort of local people. The need to adapt to climate change is obvious. The groundwater resources are in continuous decline, consequently, the surface water supplies are increasing in importance. How would the quantity of available water change in the future? How much water would we be able to store within the year to manage it during the dry seasons? Rainfall-runoff models enable us to si
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Charoenporn, Pattama. "Reservoir inflow forecasting using ID3 and C4.5 decision tree model." In 2017 3rd IEEE International Conference on Control Science and Systems Engineering (ICCSSE). IEEE, 2017. http://dx.doi.org/10.1109/ccsse.2017.8088023.

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Kim, Taesoon, Gian Choi, and Jun-Haeng Heo. "Inflow Forecasting for Real-Time Reservoir Operation Using Artificial Neural Network." In World Environmental and Water Resources Congress 2009. American Society of Civil Engineers, 2009. http://dx.doi.org/10.1061/41036(342)499.

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Wang, Wenchuan, Xiangtian Nie, and Lin Qiu. "Support Vector Machine with Particle Swarm Optimization for Reservoir Annual Inflow Forecasting." In 2010 International Conference on Artificial Intelligence and Computational Intelligence (AICI). IEEE, 2010. http://dx.doi.org/10.1109/aici.2010.45.

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Jeong, Chang-Sam, Won-Jun Koh, and Jun-Haeng Heo. "A Study on Real-Time Forecasting of Reservoir Inflow Based on Artificial Neural Network." In Watershed Management and Operations Management Conferences 2000. American Society of Civil Engineers, 2001. http://dx.doi.org/10.1061/40499(2000)82.

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Dampage, Udaya, Yasiru Gunaratne, Ovindi Bandara, Samitha De Silva, and Vinushi Waraketiya. "Artificial Neural Network for Forecasting of Daily Reservoir Inflow: Case Study of the Kotmale Reservoir in Sri Lanka." In 2020 5th International Conference on Computational Intelligence and Applications (ICCIA). IEEE, 2020. http://dx.doi.org/10.1109/iccia49625.2020.00009.

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Guo, Genliang, and R. D. Evans. "Inflow Performance and Production Forecasting of Horizontal Wells With Multiple Hydraulic Fractures in Low-Permeability Gas Reservoirs." In SPE Gas Technology Symposium. Society of Petroleum Engineers, 1993. http://dx.doi.org/10.2118/26169-ms.

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