A Multi-Algorithm Approach using Spatial Land use Data to Predict Phthalate Ester Concentrations in the Surface Water of U-Tapao Canal, Southern Thailand

Howard IC, Okpara KA and Howard CC

Published on: 2026-06-06

Abstract

Phthalate esters (PAEs) are widespread endocrine-disrupting chemicals present in the surface freshwaters of the world, yet no established modelling approach exists for the spatial prediction of PAE concentrations across the study area. Hence this, this work introduces a novel machine learning (ML)-based framework for spatial PAE prediction - dibutyl phthalate (DBP), di(2-ethylhexyl) phthalate (DEHP), diisononyl phthalate (DiNP), along with their sum (PAEs)) in the U-Tapao Canal, southern Thailand based on empirical surface water quality data measured by [1]. Empirically-derived PAE concentrations at 17 georeferenced sample locations (PAEs = 1.44-12.08 g/L) are used in combination with spatial predictors derived from a GIS land use map of the catchment. Four ML algorithms (Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM) neural network, and Support Vector Regression (SVR)) are applied to the data-set and assessed in comparison to a multiple linear regression (MLR) baseline through a purpose-built leave-one-out cross validation (LOOCV) framework optimally adapted for small sample sizes. The XGBoost algorithm recorded the closest fit to the empirical data-set (R= 0.96; RMSE= 0.35 g/L), with RF yielding a strong predictive skill (R= 0.94). Subsequent analysis of the AI algorithms' feature importances revealed that (in order of importance): 1. Distance to industrial areas, 2. Land use class, and 3. DEHP concentration was most explanatory for PAEs prediction. A combined two-stage architecture-using a binary classifier to predict the presence or absence of PAEs in a water sample, fed into a subsequent linear regressor for PAE concentration-proved most effective for handling ND data-sets without bias introduced through data imputation. Despite limited empirical surface water measurements, these results indicate that robust ML models can be constructed to predict PAE hotspots and relative contaminant levels, and lays a supportive road-mapping framework for other emerging contaminant classes in tropical watersheds.