Scholarly record
FROM MULTI-CRITERIA SUITABILITY MAPPING TO HYBRID PREDICTIVE MODELLING: AHP AND RANDOM FOREST FOR GROUNDWATER RETENTION ASSESSMENT
Abstract
Groundwater retention potential assessment is essential for sustainable water resource management, climate adaptation, and spatial planning. Previous studies based on Analytic Hierarchy Process have demonstrated the usefulness of expert-driven multi-criteria analysis for identifying areas favourable for groundwater retention using geospatial and hydrogeological data. Building upon this approach, the present study proposes a comparative and hybrid modelling framework integrating AHP and Random Forest for groundwater retention potential mapping. The research uses a 130 km2 study area characterized using high-resolution LiDAR data and hydrogeological observations from 150 monitoring wells. Terrain and environmental predictors, including Topographic Position Index, slope, hydrographic network density, land cover, and infiltration rates, are used to develop (i) a conventional AHP-based groundwater retention model, (ii) a data-driven Random Forest predictive model, and (iii) a hybrid AHP–Random Forest framework. The study investigates whether hybrid expert-driven and machine learning approaches can improve predictive accuracy over traditional multi-criteria suitability mapping while providing enhanced interpretability for environmental decision support. Expected results demonstrate that integrating expert knowledge with machine learning can significantly improve groundwater retention prediction and provide a transferable framework for hydrogeological risk assessment and climate-resilient water management. The proposed methodology represents a transition from static suitability mapping toward hybrid predictive modelling and offers a promising direction for next-generation groundwater resource assessment.
Publication details
References10
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