A smart water quality companion for farmers: predicting canal suitability for aquaculture and livestock using validated machine learning models and web based tool

Abstract

In recent times, global food production has transitioned towards aquaculture and livestock farming owing to the contamination of agricultural crops with harmful fertilisers and pesticides. Quality of intake water plays a crucial role in the growth and wellbeing of the aquatic creatures and livestock. This study aims to develop and validate a machine-learning-driven framework and an open access web tool that integrates a diurnal water quality index with optimised predictive models for aquaculture and livestock applications in the deltaic region of western Godavari, India. Critical parameters were analysed for diurnal samples over two seasons on a semi-perennial canal. From the water quality data using supervised machine learning techniques like linear regression (LR), random forest (RF), decision tree (DT), long short-term memory (LSTM), XGBoost, support vector regression (SVR), and artificial neural networks (ANN), water quality prediction models were developed and compared for model accuracy and feature parameter influence on aquaculture water quality index (AWQI) and livestock water quality index (LWQI). The results indicated that the regression and classification models of LR and SVR showed the highest accuracy when applied to aquaculture ( R 2 : 0.999, MSE: 0.0004 & R 2 : 0.999, MSE: 0.006) and livestock ( R 2 : 0.999, MSE: 0.00001 & R 2 : 0.999, MSE: 0.0043) water quality datasets. This study offers more practical benefits to aquatic and livestock farmers, field officers and experts by enabling dual water quality analysis and prediction through an open-access web tool for instantaneous, quick decision-making on water quality for their intended use.