Artificial Intelligence and Spatial Analysis in the Assessment of Water and Soil Pollution and Environmental Risk Management: A Comprehensive Literature Review

Abstract

The current global environmental studies indicate that a change in methodology is occurring from description-oriented monitoring approaches to analytical models which have greater ability to interpret the complexity of pollutants in space and time, especially in water and soil systems where natural and anthropogenic influences co-evolve. This transition has enabled the increased adoption of AI and spatial analysis alongside other technologies to uncover pollution trends, identify risk zones, and create predictive models for environmental decision-making. In this review study, recent trends in using machine learning algorithms, Geographic Information System (GIS), and remote sensing in water and soil pollution and environmental risk management are discussed, and related to the context of geographic studies in environmental fields related to water, soil, and waste pollution and environmental factors along with the issue in Iraq. Based on these findings, the study concludes that the use of artificial intelligence and spatial analysis is a new epistemological approach that brings a new way to understand the relationship between pollutant sources, spatial features, pollutant flow and environmental exposures. The study also highlights that the usefulness of these techniques is dependent on the quality of the data, the efficiency of the model and the discoverability of reliable environmental monitoring databases in space, highlighting the usefulness of these tools for the development of sustainable environmental management and early risk detection.