Elsevier

Applied Soft Computing

Volume 7, Issue 1, January 2007, Pages 265-285
Applied Soft Computing

Water allocation improvement in river basin using Adaptive Neural Fuzzy Reinforcement Learning approach

https://doi.org/10.1016/j.asoc.2005.02.007Get rights and content

Abstract

An accurate simulation model is a necessary tool for optimizing allocation of scarce water resources in large-scale river basins. Adaptive Neural Fuzzy Inference System (ANFIS) method is used to simulate seven interconnected sub-basins in a regional river system located in Iran. Simulated predictions of the method are compared with historical data measurements. ANFIS is a powerful tool for simulating water resources systems of all sub-basins. In this study, a new methodology, Adaptive Neural Fuzzy Reinforcement Learning (ANFRL) is presented for obtaining optimal values of the decision variables. By combining ANFIS with Fuzzy Reinforcement Learning within the content of historical data over a consecutive monthly management period, ANFRL method was derived. Based upon the results of this research, this methodology can be used to develop fuzzy rule systems that accurately simulate the behavior of complex river basin systems within the context of uncertainty. As previous researches have shown that, when simulation model accurately reproduces observed river basin behavior, the optimization model yields better results. Application of this approach in the present case study shows that the effects of uncertainty, imprecise and random factors are 21, 11 and 15% over water resources system, water demand estimated and hydrological regime, respectively. Finally, the results of this method showed that about 16% improvement in water allocation was attained when compared to the primary water resources management in this case study.

Introduction

Optimal use of water is an important objective of water resource development projects all over the world. An integrated approach toward better water resources management in river basins for irrigation planning is needed to find optimal water use policies. In the past, researchers used variables affecting crop pattern and reservoir releases as decision variables [1]. Labadie [2] found discrepancies in simulation and optimization models which are important factors in non-adaptive and weak system managements in river basins. These models become more complicated considering conflicting objectives, stochastic hydrology behavior, and uncertain consumptive water use. Labadie [2] presented a combined simulation-optimization strategy for river system management. In his studies, decision variable was reservoir release and objective function was maximization of power generation. However, the objective of his study was to assess directly the optimal water use.

The other group of studies is concerned with indirect optimization of water use by selecting the best strategies or alternatives in the river basin or even on the farms. Multi-objective methods have been widely used in different water resource projects. Bogardi and Nachtnebel [3] used multicriteria decision analysis in the study of water resources management. Other applications of this group can be found in the works of Karamouz et al. [4] and Owen et al. [5].

The theory of fuzzy logic provides a mechanism to represent the degree of satisfaction of reservoir objective through the use of fuzzy membership function measures that can be combined in an integrated fashion. The fuzzy approach, alluding to the vagueness or imprecision inherent in problems of this type, has found increasing application in many fields. Fontane et al. [6] applied reservoir operation based on fuzzy logic concept in order to deal with imprecise objectives for the reservoirs located in the monographic area on the Cache la Poudre river basin in the northern Colorado. Sasikumar and Mujumdar [7] developed a fuzzy waste-load allocation model (FWLAM) for water quality management of a river system using fuzzy multiple objective optimization. Dubrovin et al. [8] used a new methodology for fuzzy inference and compared it with a traditional (Sugeno style) method, for multipurpose real-time reservoir operation. In these researches, it is implicitly assumed that current decisions are independent of future events and decisions beyond the planning horizon. Besides, stochastic nature of hydrologic parameters, imprecise water demand, uncertainty of relationship between variables in groundwater and surface water resources, cannot be completely incorporated into membership functions [9], [10].

Molden and Gates [11] and Gates and Ahmed [12] developed an approach for assessing the alternative strategies for improving irrigation water delivery system in the context of multiple planning criteria. Alternatives that involve structural, managerial and policy changes have also been discussed. The model takes into account the parameter of uncertainty on both supply and demand sides of the system resulting from temporal and spatial variability and inadequate data. The objective of adequacy, efficiency, dependability and equity of water delivery were used to evaluate system performance under each alternative considered. Techniques of multicriterion decision making (MCDM) were also presented. The part of historical data is created by the decisions of experts, users (farmers), designers, and managers and is defined as “Human effects” [13]. In these researches, the effects are not completely incorporated into membership functions and the results of this method are in conflict by application of this approach. This approach has also problems in defining objectives, constraining functions or implementing models.

Increasing demands for agricultural products with limited water resources lead to water allocation and management problems. In addition, the conflicting objectives of individual monetary benefits and social benefits make the problems rather more complex. For efficient and scientific solutions of these problems, groundwater is also to be optimally extracted and combined with surface water to meet the requirements. On the other hand, uncertainty, vagueness and random factors make water allocation problems more complex in the form of unexpected droughts and floods, uncertainty in conjunctive use of surface and ground water, vagueness in water use efficiency and variation of inflows from month to month. As control problems become more complex in these applications, the use of traditional control techniques requiring mathematical models of the plant becomes more difficult to apply. Intelligent controllers have several important advantages, such as shorter development time, and less assumption about the dynamical behavior of the plant, that makes them attractive for application to these problems. Fuzzy set theory provides a mathematical framework for modeling vagueness and imprecision. Neural networks have the ability to learn complex mappings, generalize information, and classify inputs. Hybrid controllers utilize the advantages of each, as well as other novel techniques, creating a powerful tool for intelligent control [14].

The methodology that can be used in selecting the optimum decision of water allocation for each sub-basin from the previous decisions (historical data) is the basic modeling approach in this study. This method includes two steps: the first step is to prepare the simulation models of water use, and the second step is development of the optimization models of water allocation for each sub-basin. Usually, these steps are separated in the literature. In this study, models of each step are not only obtained based on compatible methodologies, but the results of each optimization model are also obtained based on the optimal values of input predictor variables which are selected from the results of simulation models over historical data. Therefore, the output values of the simulation models remain constant. In other words, the simulation models learn to minimize the error between the output and real values (observed values) by using Adaptive Neural Fuzzy Inference System (ANFIS) method. The optimization models are reinforcement learning that seeks to maximize the values of the input predictor variables subject to the fixed output values of simulation models.

For all sub-basins, river outflow was the sole prediction variable for the all simulation models. ANFIS method used different sets of input predictor variables for each sub-basin as dictated by the hydrologic factors. For example, if groundwater extraction occurred, this variable was also used as an input predictor variable, as well as decision variable.

The abilities and advantages of presented method can be explained as: (1) the direct effects of uncertain, vague and random factors over water resources system, water demand estimated and hydrological regime can be incorporated into membership function that are considered in developing the simulation and optimization models, (2) the Human effects are incorporated into membership functions, and the results of this approach will not be conflicted in the future conditions. Therefore, these effects can be quantified by using the reliabilities of previous and optimum conditions of the decision variables in this study, and (3) this method does not have problems like MCDM or economical methods in defining objectives, constraining functions or implementing models.

Section snippets

Adaptive Neural Fuzzy Inference System

An adaptive network is a network structure consisting of a number of nodes connected through direct links. Each node represents a process unit, and the links between nodes specify the causal relationship between the connected nodes. All or parts of the nodes are adaptive, which means the outputs of these nodes depend on modifiable parameters pertaining to these nodes. The learning rule specifies how these parameters should be updated to minimize a prescribed error measure, which is a

Case study: the Kor and Seevand river basin

General features. The Kor and Seevand river basin is located in the northern part of Fars province in Iran and lies between 51°45′ and 54°30′ eastern latitude and 29°01′ and 31°15′ northern longitude. Total river basin area is 31 511 km2 with 16 630 km2 of mountains and 14 881 km2 of plains and lakes. Kor river with two branches called Kor and Seevand are the artery of this river basin. These two branches join in Marvdasht area and form the main Kor river. The downstream reach flows into Bakhtegan

Results and discussion

An important objective of this study was to maximize the volume of excess water in each sub-basin or river flow in each hydrometeric station. Decision variables of optimization models included release from the dam, storage volume, river flow in the upstream sub-basin, and groundwater pumping or drainage water reused. Results of these models are presented in Table 4. In some months, optimum values of decision variables could not be found. Optimum values of decision variables were found from the

Summary and conclusions

In recent years, fuzzy logic has become a strong tool in water resources studies. The main objective of this study is to use this approach in the optimization of water use in river basins. An approach is presented for considering spatial and temporal variation in allocating water on a large-scale river basin. Using simulation models is very important in developing an optimization model in this study. The simulation model used for this purpose consisted of smaller multi-process simulation

Acknowledgments

The research leading to this paper was conducted at the Shiraz University, Iran. The measured data were collected by Fars Regional Water Authority, Iran, and Fars Agricultural Research Center, Iran. The authors are grateful to Dr. B. Zahraie from University of Tehran, Sh. Araghi-Nejhad and Reza Karachian Ph.D. Candidates of Amir Kabir University. Water and Environment Research and Development (WE-R&D) office is also greatly appreciated.

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