NeuroFuzzy modelling for conflict resolution in irrigation management
Management of large-scale irrigation schemes is more complicated than commonly recognized. Irrigation management involves complicated social, organizational, legal, and economical issues in addition to the undoubtedly important technical matters and environmental aspects. Management decisions have potential to be controversial because the involved groups (irrigation officials and water users) hold distinct interests and conflicting objectives. Irrigation plans, bureaucratically decided by irrigation officials, are often rejected by water users causing additional conflicts in managing the system and adversely affecting the schemes' sustainability. It is believed that involving water user associations at different stages of management is a key mechanism towards the improvement of irrigation systems. Therefore, participatory management of irrigation systems has long been an objective for many authorities. However, little has been done to support participation of both groups. In response to this issue, the current work is devoted to introduce a framework for participatory planning of seasonal management decisions and to propose a model, which enables assessment of planning alternatives. Based on a detailed system analysis, the elements of the planning decisions (alternatives, restrictions, and objectives) are identified, and in turn, a set of performance indicators are defined to quantify the conflicting objectives of the groups involved. The proposed framework suggests a committee consisting of scheme managers and farmers. The committee is responsible for defining planning alternatives, simulating them, and assessing their economical, technical, social, and environmental performance. The assessment involves conflicting indicators with a high level of uncertainty and noncommensurability as well as competing interests and vague viewpoints of involved decision makers (DMs). Consequently, the situation is formulated mathematically as a Fuzzy-Multiple-Participant- Multiple-Criteria Decision Making Problem. A three-level hierarchical structure is adopted to formulate the interrelationship of the indicators and to aggregate them in order to obtain a single value that reflects the overall performance of each suggested plan. An assessment model is developed using a combination of both Artificial Neural Networks (ANNs) and Fuzzy Logic (FL) to build a so-called NeuroFuzzy Assessment Model (NFAM). The NFAM utilizes the architecture of ANNs to represent the suggested hierarchical structure of indicators. The learning capability of the ANNs is employed to capture the conflicting assessment opinions of DMs. An Analytic Hierarchy Process (AHP) is applied to weight the involved DMs with respect to their experience and qualification. The weights are taken into consideration when training sets are formulated for tuning the model. FL is used to handle the uncertainty, vagueness, and the non- commensurability related to the aggregation of the indicators within a strict mathematical framework. Finally, a numerical example is performed to demonstrate the feasibility of the proposed NFAM. The framework is considered to be an important contribution towards resolving conflicts in irrigation management, since it identifies both roles of participants and rules for their participation. The developed NFAM is an intelligent, simple and flexible tool to assess planning alternatives. The current work encourages researchers to utilize the NeuroFuzzy technology in order to enhance modeling of water resources problems, especially in ecohydrological modeling.
info:eu-repo/classification/ddc/630, Conflict Resolution, Fuzzy Logic, Irrigation Management, Neural Networks, Neuro-Fuzzy Systems, Participatory Management, Bewässerungssystem, Neuro-Fuzzy-System, Landwirtschaft, Veterinärmedizin
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