A Supervising Grid Model for Identification of Groundwater Pollute

Abstract

Groundwater asset is the most critical freshwater asset. A large portion of the number of inhabitants in our nation relies upon this asset for their essential needs of water. Groundwater has assumed a noteworthy part in expanding nourishment generation and accomplishing sustenance security. Groundwater, an inexhaustible wellspring of water, has an exceedingly reliable water supply for horticulture, household, business, and mechanical needs. Groundwater sullying is a significant issue in our reality. Optimal groundwater monitoring network design models are developed to determine the mass estimation error of contaminant concentration over different management time periods in groundwater aquifers. The objective of the paper is to determine the mass estimation error of contamination concentration at 2.5 years and 7.5 years. The mass estimation error of contamination concentration over time is determined by using the various computer software such as Method of Characteristics (MOC, USGS), Surfer 7.0, and Simulated Annealing (SA). The Method of Characteristics (MOC) is used in this model to solve the solute-transport equation. Simulated annealing is a worldwide improvement strategy that is utilized to locate optimal monitoring well locations. The error of the estimated concentration at potential well locations is extrapolated over the entire study area by geostatistical instrument, kriging. The outlined observing system is dynamic in nature, as it gives time-shifting system plans to various management periods. The optimal monitoring wells design incorporates budgetary constraints in the form of limits on the number of monitoring wells installed in any particular management period. The solution results are evaluated for an illustrative study area comprising of a hypothetical aquifer. The performance evaluation results establish the potential applicability of the proposed methodology for the optimal design of the dynamic monitoring networks for determining the mass estimation error of contamination concentration.KeywordsDynamic monitoring network designGroundwater contaminationContamination detectionMOCOptimizationSimulated annealingKrigingMass estimation error