Performances of MLR, RBF-NN, and MLP-NN in the evaluation and prediction of water resources quality for irrigation purposes under two modeling scenarios

Abstract

One of the pivotal decision-making tools for sustainable management of water resources for various uses is accurate prediction of water quality. In the present paper, multiple linear regression (MLR), radial basis function neural network (RBF-NN), and multilayer perceptron neural network (MLP-NN) models were developed for the monitoring and management of irrigation water quality (IWQ) in Ojoto area, southeastern Nigeria. This paper is the first to integrate and simultaneously implement these predictive methods for the modeling of seven IWQ indices. Moreover, two modeling scenarios were considered. Scenario 1 represents predictions that utilized the specific physicochemical parameters for calculating the IWQ indices as input variables while Scenario 2 represents predictions that utilized pH, EC, Na⁺, K⁺, Mg²⁺, Ca²⁺, Cl^-, SO₄^2-, and HCO₃^- as inputs. In terms of salinity hazard, most of the water resources are unsuitable/poor for irrigation. However, in terms of carbonate and bicarbonate impact and magnesium hazard, majority of the samples have good and excellent IWQ. Seven agglomerative Q-mode dendrograms spatiotemporally classified the water resources based on the IWQ indices. Model validation metrics showed that the MLR, RBF-NN, and MLP-NN models developed in the two scenarios performed well in both scenarios, with minor variations.

Keywords:

• Artificial intelligence
• irrigation water quality indexing
• multilayer perceptron
• multiple linear regression
• radial basis function
• water quality prediction

Disclosure statement

The authors hereby declare that there are no known competing interests.