Path of Science: International Electronic Scientific Journal

Groundwater is vital to industry, agriculture, and drinking water production. A growing amount of groundwater needs to be managed effectively because of the effects of climate change and growing demand. Conventional methods frequently prove inadequate for managing groundwater and tackling these issues. This study investigates how to use machine learning, Internet of Things (IoT) technologies, and civil engineering to create a more reliable and effective groundwater management strategy and Infrastructure in our environments. Real-time monitoring capabilities offered by IoT technology allow for ongoing data collection on groundwater levels, quality, and usage. Machine learning algorithms can use this data to forecast future patterns and anomalies, providing an initiative-taking groundwater management tool. Civil engineering solutions like artificial recharge and sophisticated irrigation systems are crucial for sustainable usage and replenishment. This paper thoroughly analyzes current developments in various domains and suggests a synergistic framework to improve groundwater management by fusing machine learning, IoT, and civil engineering. According to our research, integrating these technologies can maximize groundwater resource utilization, raise aquifer sustainability, and increase the accuracy of groundwater monitoring and forecasting. The suggested framework offers a comprehensive and innovative technological solution to overcome the shortcomings of current groundwater management techniques. Future research should concentrate on improving integrated systems and investigating their applications across various geographical and climatic contexts to ensure the sustainable management of groundwater resources globally.

Management of groundwater; The Internet of Things; Monitoring in real time; Machine learning; Civil engineering; Optimization of water resources; Adaptation to climate change

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