AI-Driven Predictive Modelling for Groundwater Salinization in Israel

2026-02-07

AI Summary

Groundwater salinization poses a critical threat to water security, necessitating a clear understanding of its multidimensional drivers. This study presents the first national-scale assessment integrating multi-source data within a predictive framework that combines random forest, XGBoost, and neural network models. To systematically identify key drivers, the framework incorporates causal inference and explainable artificial intelligence (XAI) techniques—including recursive feature elimination (RFE), global sensitivity analysis (GSA), SHAP values, and double machine learning. The results reveal precipitation, temperature, distance to saline water bodies, agricultural area, and reclaimed water use as dominant factors, with reclaimed water playing a particularly crucial role in hydroclimatically vulnerable regions. This insight substantially reduces model uncertainty and provides a robust scientific foundation for effective salinization mitigation strategies.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Humans and AI: Explainable AI (XAI) for Human Understanding

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Increasing salinity and contamination of groundwater is a serious issue in many parts of the world, causing degradation of water resources. The aim of this work is to form a comprehensive understanding of groundwater salinization underlying causal factors and identify important meteorological, geological and anthropogenic drivers of salinity. We have integrated different datasets of potential covariates, to create a robust framework for machine learning based predictive models including Random Forest (RF), XGBoost, Neural network, Long Short-Term Memory (LSTM), convolution neural network (CNN) and linear regression (LR), of groundwater salinity. Additionally, Recursive Feature Elimination (RFE) followed by Global sensitivity analysis (GSA) and Explainable AI (XAI) based SHapley Additive exPlanations (SHAP) were used to estimate the importance scores and find insights into the drivers of salinization. We also did causality analysis via Double machine learning using various predictive models. From these analyses, key meteorological (Precipitation, Temperature), geological (Distance from river, Distance to saline body, TWI, Shoreline distance), and anthropogenic (Area of agriculture field, Treated Wastewater) covariates are identified to be influential drivers of groundwater salinity across Israel. XAI analysis also identified Treated Wastewater (TWW) as an essential anthropogenic driver of salinity, its significance being context-dependent but critical in vulnerable hydro-climatic environment. Our approach provides deeper insight into global salinization mechanisms at country scale, reducing AI model uncertainty and highlighting the need for tailored strategies to address salinity.

Problem

• groundwater salinization
• causal factors
• meteorological drivers
• geological drivers
• anthropogenic drivers

Innovation

• Explainable AI (XAI)
• Groundwater salinization
• Machine learning
• Causal inference
• Feature importance