Title: "Determination of illegal pumping and monitoring network using genetic algorithm based simulation-optimization model"
         

DOI: 10.15224/978-1-63248-065-1-50
Page(s): 86 - 90
Authors: ARNESH DAS, RAJEEV GANDHI BG, RAJIB KUMAR BHATTACHARJYA

Abstract

Groundwater is an important source of freshwater, more so in areas away from the surface water sources. Due to the substantial growth in industry and agriculture as well as the increased use of municipal water, the demand of groundwater has been increasing continuously in many parts of the world. This has depleted the groundwater table in many parts of the country as well as in other parts of the world. As a result of over exploitation of groundwater, the quality of groundwater is also deteriorating rapidly. Hence there is a need to monitor the exploitation of groundwater in an aquifer vulnerable from quality and quantity aspects. The overexploitation or the illegal pumping of groundwater can be assessed by using inverse optimization techniques. In this study, an inverse optimization model is proposed to identify the illegal pumping locations and pumping rates. The performance of the model is highly related to the location and number of monitoring wells used in the model. As such, a modified formulation is also used to design an optimal monitoring network. We used Genetic algorithms to solve the inverse optimization model. For obtaining physically meaningful solution, the groundwater simulation model needs to incorporate with the optimization model. The simulation model simulates the physical processes in the groundwater aquifer by solving the governing groundwater flow equation. We solve the groundwater flow equation using finite difference approach. The model is then linked with the optimization model to determine the location and pumping schedule of the wells. The applicability of the proposed methodology is evaluated using a hypothetical study area involving a two dimensional aquifer. The evaluation shows that this methodology can be used for solving practical problems in the real world.