Predicting and forecasting water quality using deep learning. (17th January 2023)

• Record Type: Journal Article
• Authors: Debow, Ahmad; Shweikani, Samaah; Aljoumaa, Kadan
• Is Part Of: International journal of sustainable agricultural management and informatics. Volume 9:Number 2(2023)
• Page Start: 114
• Page End: 135
• Publication Date: 2023-01-17
• Subjects: chi-squared -- correlation matrix -- deep LSTM -- dissolved oxygen -- forecasting -- faecal coliform -- K-NN -- prediction -- total coliform -- water quality index -- WQI -- water quality class; Sustainable agriculture -- Management -- Periodicals; Sustainable agriculture -- Data processing -- Periodicals; Agricultural productivity -- Management -- Periodicals; Agricultural productivity -- Data processing -- Periodicals; Agricultural resources -- Data processing -- Periodicals; Agricultural systems -- Management -- Periodicals; Agricultural systems -- Data processing -- Periodicals; 338.1
• Languages: English
• ISSNs: 2054-5819

Abstract

During the last years, obtaining water with acceptable quality for human consumption or even for agricultural applications is a big challenge in many places around the world. Water quality (WQ) can be defined by various factors like pH, turbidity, dissolved oxygen (DO), nitrate, temperature, total and faecal coliform. Therefore, prediction and forecast of WQ have become vital in order to monitor and control pollution. In this paper, 4-stacked LSTM models are developed to predict and forecast water quality index (WQI). Many algorithms are applied in this context to prepare the data like K-NN and annual mean, also for data analysis and features selection. The best prediction model is to predict without total coliform and RMSE value is 0.027, and the best forecasting method is filtering data with RMSE = 0.013. Models in this research can contribute in water management to avoid pollution as possible as we can.