IoT-based automated system for water-related disease prediction

Abstract

Having access to potable water is a fundamental right to well-being. Despite this, 3.4 million people die from diseases caused by water each year, and 1.1 billion people lack access to potable drinking water. Although industrialization, durable infrastructure, and rapid development have increased living standards, the water problem has left humanity defenseless. As different human activities have contaminated these water reserves, according to an estimate, water is the cause of 80% of ailments. As a result, it is necessary to permit enough infrastructure to ensure the security of a reliable supply of potable water. Thus, a real-time WBPCB dataset with 17 features and a proposed IoT-based system to collect data are used in this research to address the issue. The research paper provides a system for predicting diseases and forecasting long-term trends. Classification is performed using Random Forest, XGBoost, and AdaBoost, which have accuracy rates of 99.66%, 99.52%, and 99.64%, respectively. Forecasting is performed using LSTM, which has an MSE value for the pH parameter of 0.1631. The paper introduces TS-SMOTE, a novel hybridized time-series SMOTE data augmentation approach. Additionally, it offers an IoT system that uses H-ANFIS to gather data in real-time and identify attacks.