Journal ArticleDOI
Multireservoir Modeling with Dynamic Programming and Neural Networks
TLDR
The multireservoir model based on the dynamic programming-neural network algorithm gives improved performance in this study.Abstract:
For optimal multireservoir operation, a dynamic programming-based neural network model is developed in this study. In the suggested model, multireservoir operating rules are derived using a feedforward neural network from the results of three state variables' dynamic programming algorithm. The training of the neural network is done using a supervised learning approach with the back-propagation algorithm. A multireservoir system called the Parambikulam Aliyar Project system is used for this study. The performance of the new multireservoir model is compared with (1) the regression-based approach used for deriving the multireservoir operating rules from optimization results; and (2) the single-reservoir dynamic programming-neural network model approach. The multireservoir model based on the dynamic programming-neural network algorithm gives improved performance in this study.read more
Citations
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Journal ArticleDOI
Application of back propagation neural network in predicting palm oil mill emission
TL;DR: In this paper, the authors used a neural network to simulate the process of combustion and stack gases in a palm oil mill and found that the trained data by NN agrees well with the measured data, i.e. almost within 8% error for pollutants like CO, SO2, NO and particulate matters.
Book ChapterDOI
Controlled Approximation of the Stochastic Dynamic Programming Value Function for Multi-Reservoir Systems
TL;DR: In this article, an approximation of the Stochastic Dynamic Programming (SDP) value function based on a partition of the state space into simplices is presented, where the vertices of such simplices form an irregular grid over which the value function is computed.
Journal ArticleDOI
Improved Implicit Stochastic Optimization technique under drought conditions: the case study of Agri–Sinni water system
TL;DR: In a participatory and integrated risk management approach to drought events, the reservoir OR defined in the MISO approach based on correlations between releases, storages and inflows performed better than the actual OR in the Agri–Sinni water system and the OR from a simulation-alone procedure.
Book ChapterDOI
Derivation of Operation Rules for an Irrigation Water Supply System by Multiple Linear Regression and Neural Networks
TL;DR: Results show that neural networks approach appears to improve the reservoir operation and that operating rules based on optimisation with constraints resembling real system management criteria, yield good performance both in normal and in drought periods, reducing maximum deficits and water spills.
Journal ArticleDOI
Enhanced Artificial Neural Networks for Salinity Estimation and Forecasting in the Sacramento-San Joaquin Delta of California
Siyu Qi,Zhaojun Bai,Zhi Ding,Nimal C. Jayasundara,Minxue He,Prabhjot Sandhu,Sanjaya A. Seneviratne,Tariq N. Kadir +7 more
TL;DR: In this article, domain-specific architectures of artificial neural networks (ANNs) have been developed to estimate salinity levels for planning at key monitoring stations in the Sacramento-San Joaquin Delt.
References
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Journal ArticleDOI
Reservoir Management and Operations Models: A State‐of‐the‐Art Review
TL;DR: The objective of this paper is to review the state-of-the-art of mathematical models developed for reservoir operations, including simulation, which include linear programming, dynamic programming, nonliner programming, and simulation.
Journal ArticleDOI
Rainfall forecasting in space and time using a neural network
TL;DR: A neural network is developed to forecast rainfall intensity fields in space and time using a three-layer learning network with input, hidden, and output layers and is shown to perform well when a relatively large number of hidden nodes are utilized.
Journal ArticleDOI
Dynamic programming applications in water resources
TL;DR: In this paper, the authors present a survey of dynamic programming models for water resource problems and examine computational techniques which have been used to obtain solutions to these problems, including aqueduct design, irrigation system control, project development, water quality maintenance, and reservoir operations analysis.
Journal ArticleDOI
Reservoir‐System Simulation and Optimization Models
TL;DR: A broad array of computer models have been developed for evaluating reservoir operations as discussed by the authors, and selecting a modeling and analysis approach for a particular application depends upon the characteristics of the reservoir characteristics.
Journal ArticleDOI
Neural-Network Models of Rainfall-Runoff Process
Jason Smith,Robert N. Eli +1 more
TL;DR: In this article, a back-propagation neural network is trained to predict the peak discharge and the time of peak resulting from a single rainfall pattern, and the neural network was trained to map a time series of three rainfall patterns into a continuum of discharges over future time by using a discrete Fourier series fit to the runoff hydrograph.