New approach to optimize groundwater usage using multi-criteria decision making techniques

Abstract

Chronic pumping of water from aquifers leads to groundwater depletion, leading to an alarming scarcity of groundwater supply. Early detection of low aquifers levels can prevent the complete exhaustion of groundwater. We propose a Groundwater Optimization model using Multi-Criteria Decision Making (MCDM) techniques based on field values of bore wells like aquifer level, availability of nearby bore wells, and depletion to recharge ratio. In order to achieve higher accuracy than the existing models, we take both predefined values and on-site values of individual bore wells. The hardware components include a calibrated pressure transducer, water sensor, GSM module, Raspberry Pi, and IoT module. They are used in order to send the in-field data. When the aquifer level drops below the threshold level, an SMS alert is sent to the owner of the bore well. The threshold level is dynamically determined using the key steps of MCDM methods. The MCDM methods are implemented in Python code to determine and fix the threshold for the instance. The frequency of data logging is adjusted by studying the input patterns, which saves storage. When the water level drops below the threshold level an aquifer recharge technique is sent as a recommendation. The proposed optimization model has the potential to be applied to a larger geographical area in the future.