Geo-Ecological Factors, Mathematical Models, and Algorithms in Creating a Hybrid Neural Network Training Dataset for an Intelligent Groundwater Monitoring System
Abstract
Ensuring food security in Uzbekistan and the wider Central Asian region requires sustainable water resource management under the pressures of climate change, population growth, and industrial development. Groundwater, which provides around 60% of drinking water and a significant share of irrigation supply, is of strategic importance. This study proposes the development of an intelligent computing system for groundwater monitoring, integrating sensors, networks, databases, and artificial intelligence algorithms. Due to the scarcity, fragmentation, and closed nature of groundwater data, the research emphasizes the generation of synthetic datasets that realistically reflect hydrogeological and geo-ecological variability. A hybrid modeling framework is established to simulate seasonal water level fluctuations, temperature cycles, pH dynamics, turbidity, pressure, and impedance spectra, incorporating the impacts of industrial activity and the Aral Sea. Statistical-process models and geo-ecological weighting functions are used to calibrate pollutant sources and anomaly scenarios. The synthetic datasets are validated through seasonal and anomalous test cases, ensuring distributional stability and training suitability for neural networks. The results provide a scalable approach for enhancing groundwater monitoring in Uzbekistan, supporting sustainable water management and contributing to ecological balance, agricultural efficiency, and food security.