Sustainable groundwater development using semi-supervised learning and community-led total forestry and pasture approach

Abstract

Groundwater is a critical resource for billions of people worldwide, providing a vital source of freshwater for agriculture, industry, and domestic use. However, over-extraction and mismanagement of groundwater resources have led to significant environmental and social challenges. This research presents a novel approach to address these issues through the integration of Semi-Supervised Learning (SSL) and the Community-Led Total Forestry and Pasture (CLTFP) approach. Our proposed methodology combines SSL techniques to predict and monitor groundwater levels, enabling more informed decision-making by authorities and stakeholders. By leveraging both labeled and unlabeled data, two different components including SSL and CLTFP are used, where SSL enhances the accuracy of predictions while reducing the need for costly and time-consuming data collection efforts. Furthermore, we advocate for the CLTFP approach, which promotes community engagement and sustainable land management practices. This strategy empowers local communities to actively participate in safeguarding groundwater resources, promoting sustainable farming, and reforestation efforts. The proposed method outperforms existing methods, achieving 98% accuracy, significant cost reduction, strong community engagement, and environmentally sustainable outcomes.