An important factor influencing the global environment and public health scene is the Water Quality Index (WQI). Better water quality is correlated with a higher WQI, which benefits sustainable economic growth, environmental preservation, and public health. This emphasizes how crucial it is to preserve and improve water quality globally. The degree of pollution in the air, water, sound, etc. is rising because of the fast industrial growth. The quality of drinking water is determined by the Water Quality Index. The chapter will examine the various machine-learning techniques used to determine the quality of the water. The effectiveness of well-known machine learning methods, such as neural networks, decision trees, and gradient boosting (CatBoost), is being investigated to determine the quality of water samples gathered from various sources. About 68% accuracy is achieved using gradient boosting (CatBoost). Almost all the classifiers give an accuracy greater than 61%.
Top1. Introduction
The water quality index (WQI) calculation is the most effective method for determining the quality of drinking water obtained from different areas like urban, rural, and industrial areas. The calculation of the Water Quality Index relies on several factors such as pH level, hardness, chloramines concentration, sulphate levels, conductivity, organic carbon content, trihalomethanes presence, turbidity, among others. These parameters serve as indicators for evaluating water quality and offer valuable insights, albeit not enforced by regulations. While WQI is utilised for streams, springs, and naturally occurring tea- and coffee-colored/black waters, TSI is used for lakes and estuaries. Chemical pollutants in drinking water sources are dangerous to human health since they can have quick health effects and are easily contaminated. These pollutants are a result of industrial, geological, and agricultural processes as well as human activity. Urbanisation is a major issue in the modern era that affects water quality because of overuse and improper disposal of water resources. As a result, drinking water safety is a growing concern everywhere in the world, and various physiochemical parameters were taken into consideration for the assessment of drinking water quality. Different machine learning algorithms are currently being employed for decision-making in various sectors.
Assessing water quality is crucial for protecting both human health and ecosystem integrity. Access to clean water is vital for drinking, recreational activities, and maintaining aquatic biodiversity. Through continuous monitoring, water supplies can be safeguarded against contaminants such as heavy metals and pathogens, preventing the spread of waterborne diseases and potential health emergencies. Furthermore, monitoring efforts help mitigate pollution, preserving the balance and diversity of ecosystems. Agriculture and food safety also benefit from water quality assessment, as contaminated irrigation water can compromise crop safety and consumer health. Additionally, industries reliant on clean water, such as tourism and fishing, are shielded from economic losses. Environmental regulations rely heavily on water quality monitoring to ensure adherence and prompt corrective measures when necessary. Given the challenges posed by climate change, understanding shifts in water quality becomes even more critical for effective mitigation strategies. Ultimately, water quality assessment plays a vital role in sustaining life, supporting various industries, and enhancing the resilience of ecosystems in the face of environmental pressures.