Deriving a General Operating Policy for Reservoirs Using Neural Network
01 Sep 1996-Journal of Water Resources Planning and Management (American Society of Civil Engineers)-Vol. 122, Iss: 5, pp 342-347
TL;DR: In this paper, a dynamic programming (DP) model was used to improve the operation and efficient management of available water for the Aliyar Dam in Tamil Nadu, India, using a neural network procedure (DPN) and using a multiple linear regression procedure (DPR) model.
Abstract: Reservoir operating policies are derived to improve the operation and efficient management of available water for the Aliyar Dam in Tamil Nadu, India, using a dynamic programming (DP) model, a stochastic dynamic programming (SDP) model, and a standard operating policy (SOP). The objective function for this case study is to minimize the squared deficit of the release from the irrigation demand. From the DP algorithm, general operating policies are derived using a neural network procedure (DPN model), and using a multiple linear regression procedure (DPR model). The DP functional equation is solved for 20 years of fortnightly historic data. The field irrigation demand is computed for this study by the modified Penman method with daily meteorological data. The performance of the DPR, DPN, SDP, and SOP models are compared for three years of historic data, using the proposed objective function. The neural network procedure based on the dynamic programming algorithm provided better performance than the other mo...
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TL;DR: Application of heuristic programming methods using evolutionary and genetic algorithms are described, along with application of neural networks and fuzzy rule-based systems for inferring reservoir system operating rules, to assess the state of the art in optimization of reservoir system management and operations.
Abstract: With construction of new large-scale water storage projects on the wane in the U.S. and other developed countries, attention must focus on improving the operational effectiveness and efficiency of existing reservoir systems for maximizing the beneficial uses of these projects. Optimal coordination of the many facets of reservoir systems requires the assistance of computer modeling tools to provide information for rational management and operational decisions. The purpose of this review is to assess the state-of-the-art in optimization of reservoir system management and operations and consider future directions for additional research and application. Optimization methods designed to prevail over the high-dimensional, dynamic, nonlinear, and stochastic characteristics of reservoir systems are scrutinized, as well as extensions into multiobjective optimization. Application of heuristic programming methods using evolutionary and genetic algorithms are described, along with application of neural networks and fuzzy rule-based systems for inferring reservoir system operating rules.
1,484 citations
TL;DR: The role of ANNs in various branches of hydrology has been examined here and it is suggested that ANNs should be considered as a “bridge network” to other types of neural networks.
Abstract: This paper forms the second part of the series on application of artificial neural networks (ANNs) in hydrology. The role of ANNs in various branches of hydrology has been examined here. It is foun...
1,106 citations
TL;DR: In this article, two ANN models were identified, validated and tested for the computation of dissolved oxygen (DO) and biochemical oxygen demand (BOD) concentrations in the Gomti river water.
553 citations
TL;DR: It is demonstrated that the ANFIS can be applied successfully and provide high accuracy and reliability for reservoir water level forecasting in the next three hours and the model with human decision as input variable has consistently superior performance with regard to all used indexes than the model without this input.
521 citations
Cites methods from "Deriving a General Operating Policy..."
...In the hydrological forecasting context, ANNs have also proven to be an efficient alternative to traditional methods for rainfall forecasting [13,15,26], streamflow forecasting [2–4,6,10,18,22,36,39], groundwater modeling [24,37], and reservoir operation [17,19,30]....
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Journal Article•
TL;DR: The research described in this article investigates the utility of Artificial Neural Networks for short term forecasting of streamflow and compares the performance of this tool to conventional approaches used to forecast streamflow.
Abstract: The research described in this article investigates the utility of Artificial Neural Networks (ANNs) for short term forecasting of streamflow. The work explores the capabilities of ANNs and compares the performance of this tool to conventional approaches used to forecast streamflow. Several issues associated with the use of an ANN are examined including the type of input data and the number, and the size of hidden layer(s) to be included in the network. Perceived strengths of ANNs are the capability for representing complex, non-linear relationships as well as being able to model interaction effects. The application of the ANN approach is to a portion of the Winnipeg River system in Northwest Ontario, Canada. Forecasting was conducted on a catchment area of approximately 20 000 km2. using quarter monthly time intervals. The results were most promising. A very close fit was obtained during the calibration (training) phase and the ANNs developed consistently outperformed a conventional model during the verification (testing) phase for all of the four forecast lead-times. The average improvement in the root mean squared error (RMSE) for the 8 years of test data varied from 5 cms in the four time step ahead forecasts to 12.1 cms in the two time step ahead forecasts.
461 citations
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17 Jul 1986
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2,428 citations
01 Jan 1977
TL;DR: Guidelines for predicting crop water requirements as mentioned in this paper, which are based on guidelines for predicting water requirements, are used to predict water requirements in the field of crop water forecasting, and they can be found in Table 1.
Abstract: Guidelines for predicting crop water requirements , Guidelines for predicting crop water requirements , مرکز فناوری اطلاعات و اطلاع رسانی کشاورزی
2,098 citations
Book•
01 Jan 1977
TL;DR: Guidelines for predicting crop water requirements as discussed by the authors, which are based on guidelines for predicting water requirements, are used to predict water requirements in the field of crop water forecasting, and they can be found in Table 1.
Abstract: Guidelines for predicting crop water requirements , Guidelines for predicting crop water requirements , مرکز فناوری اطلاعات و اطلاع رسانی کشاورزی
1,780 citations