IoT-enabled groundwater monitoring with k-NN-SVM algorithm for sustainable water management

Groundwater is a critical resource for sustaining agriculture, industry, and human life. However, overexploitation and contamination of groundwater resources have led to significant environmental and socio-economic challenges. To address these challenges, there is a need for effective groundwater management strategies that utilize advanced technologies such as the Internet of Things (IoT) and machine learning algorithms. The research proposes an IoT-enabled groundwater monitoring system that utilizes K-Nearest Neighbors (k-NN) and Support Vector Machines (SVM) algorithms for sustainable water management. The system comprises IoT sensors that monitor groundwater levels, quality, and flow rates in real time, and transmit the data to a centralized server for analysis. The k-NN algorithm is used for initial data clustering, while SVM is used for classification and prediction tasks related to groundwater data. The system is designed to identify anomalies, detect trends, and provide insights into the usage and availability of groundwater resources. The proposed system can help in promoting sustainable water use practices, reducing water wastage, and optimizing groundwater usage. The research findings can be useful for water managers, policymakers, and other stakeholders in the agriculture and industrial sectors who are concerned with sustainable groundwater management.