Modelling and Prediction of Water Quality by Using Artificial Intelligence

Mosleh Hmoud Al-Adhaileh and Fawaz Waselallah Alsaade
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Mosleh Hmoud Al-Adhaileh: Deanship of E-Learning and Distance Education King Faisal University Saudi Arabia, Al-Ahsa P.O. Box 4000, Saudi Arabia
Fawaz Waselallah Alsaade: College of Computer Science and Information Technology, King Faisal University, Al-Ahsa P.O. Box 4000, Saudi Arabia

Sustainability, 2021, vol. 13, issue 8, 1-18

Abstract: Artificial intelligence methods can remarkably reduce costs for water supply and sanitation systems and help ensure compliance with the quality of drinking and wastewater treatment. Therefore, modelling and predicting water quality to control water pollution has been widely researched. The novelty of the proposed system is presented to develop an efficient operation of monitoring drinking water to ensure a sustainable and friendly green environment. In this work, the adaptive neuro-fuzzy inference system (ANFIS) algorithm was developed to predict the water quality index (WQI). Feed-forward neural network (FFNN) and K-nearest neighbors were applied to classify water quality. The dataset has eight significant parameters, but seven parameters were considered to show significant values. The proposed methodology was developed based on these statistical parameters. Prediction results demonstrated that the ANFIS model was superior for the prediction of WQI values. Nevertheless, the FFNN algorithm achieved the highest accuracy (100%) for water quality classification (WQC). Furthermore, the ANFIS model accurately predicted WQI, and the FFNN model showed superior robustness in classifying the WQC. In addition, the ANFIS model showed accuracy during the testing phase, with a regression coefficient of 96.17% for predicting WQI, and the FFNN model achieved the highest accuracy (100%) for WQC. This proposed method, using advanced artificial intelligence, can aid in water treatment and management.

Keywords: water quality; water quality index; water quality classification; adaptive neuro-fuzzy inference system; feed-forward neural network models (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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