Modelling Intelligent Water Resources Allocation for Multi-users
Journal
Water Resources Management
Journal Volume
30
Journal Issue
4
Pages
1395-1413
Date Issued
2016
Author(s)
Abstract
This study proposes intelligent water resources allocation strategies for multiple users through hybrid artificial intelligence techniques implemented for reservoir operation optimization and water shortage rate estimation. A two-fold scheme is developed for (1) knowledge acquisition through searching input–output patterns of optimal reservoir operation by optimization methods and (2) the inference system through mapping the current input pattern to estimate the water shortage rate by artificial neural networks (ANNs). The Shihmen Reservoir in northern Taiwan is the study case. We first design nine possible water demand conditions by investigating the changes in historical water supply. With the nine designed conditions and 44-year historical 10-day reservoir inflow data collected during the growth season (3 months) of the first paddy crop, we first conduct the optimization search of reservoir operation by using the non-dominated sorting genetic algorithm-II (NSGA-II) in consideration of agricultural and public water demands simultaneously. The simulation method is used as a comparative model to the NSGA-II. Results demonstrate that the NSGA-II can suitably search the optimal water allocation series and obtain much lower seasonal water shortage rates than those of the simulation method. Then seasonal water shortage rates in response to future water demands for both sectors are estimated by using the adaptive network fuzzy inference system (ANFIS). The back-propagation neural network (BPNN) is adopted as a comparative model to the ANFIS. During model construction, future water demands, predicted monthly inflows (or seasonal inflow) of the reservoir in the next coming quarter and historical initial reservoir storages configure the input patterns while the optimal seasonal water shortage rates obtained from the NSGA-II serve as output targets (training targets) for both neural networks. Results indicate that the ANFIS and the BPNN models produce almost equally good performance in estimating water shortage rates, yet the ANFIS model produces even better stability. The reliability of the proposed scheme is further examined by scenario analysis. The scenario analysis indicates that an increase in public water demand or a decrease in agricultural water demand would bring more impacts of water supply on agricultural sectors than public sectors. Similarly, a bigger decrease in inflow amount would obviously bring more influence on agricultural sectors than public one. Consequently, given predicted inflow, decision makers can pre-experience the possible outcomes in response to competing water demands through the estimation models in order to determine adequate water supply as well as preparedness measures, if needed, for drought mitigation. © 2016, Springer Science+Business Media Dordrecht.
Subjects
Adaptive network fuzzy inference system (ANFIS); Artificial neural network (ANN); Back-propagation neural network (BPNN); Multi-objective reservoir operation; Non-dominated sorting genetic algorithm-II (NSGA-II); Water allocation
Other Subjects
Agriculture; Artificial intelligence; Backpropagation; Backpropagation algorithms; Decision making; Fuzzy inference; Fuzzy logic; Fuzzy systems; Genetic algorithms; Inference engines; Neural networks; Optimization; Reliability analysis; Reservoirs (water); Resource allocation; Sorting; Torsional stress; Water supply; Adaptive network fuzzy inference systems; Back-propagation neural networks; Non dominated sorting genetic algorithm ii (NSGA II); Reservoir operation; Water allocations; Water resources; artificial intelligence; artificial neural network; back propagation; genetic algorithm; optimization; reservoir; resource allocation; water resource; Shihmen Reservoir; Taiwan
Type
journal article