Evaluation and prediction of irrigation water quality of an agricultural district, SE Nigeria: an integrated heuristic GIS-based and machine learning approach

AbstractPoor irrigation water quality can mar agricultural productivity. Appraising the irrigation water quality requires the computation of various conventional quality parameters which are often time-consuming and associated with errors during sub-index computation. It becomes critical therefore, to have a visual assessment of the irrigation water quality and identify the most influential water quality parameters for accurate prediction, management, and sustainability of irrigation water quality. The overlay weighted sum technique was used to generate the irrigation water quality (IWQ) map of the area. The map revealed that 72.5% of the area (within the southeastern parts) were suitable for irrigation while 28.4% (found in isolated traces) were unsuitable. Multilayer perceptron artificial neural networks (MLP-ANNs) and multiple linear regression models (MLR) were integrated and validated to predict the IWQ parameters using Cl−, HCO3−SO42−, NO3−, Ca2+, Mg2+, Na+, K+, pH, EC, TH and TDS as input variables, and PI, MAR, SAR, PI, KR, SSP, and PS as output variables. The two models showed high performance accuracy based on the results of the coefficient of determination (R2 = 0.513–0.983). Low modeling errors were observed from results of the sum of square errors (SOSE), relative errors (RE), adjusted R-square (R2adj), and residual plots; further confirming the efficacy of the two models, although the MLP-ANNs showed higher prediction accuracy with respect toR2. Based on the sensitivity of the MLP-ANN model, HCO3, pH, SO4, EC, and Cl were identified to have the greatest influence on the irrigation water quality of the area. This study has shown that the integration of GIS and Machine Learning can serve as rapid decision tools for proper planning and enhanced agricultural productivity.