1. Introduction
Water is the most significant resource of life, crucial for supporting the life of most existing creatures and human beings. Living organisms need water with enough quality to continue their lives. There are certain limits of pollutions that water species can tolerate. Exceeding these limits affects the existence of these creatures and threatens their lives.
Most ambient water bodies such as rivers, lakes, and streams have specific quality standards that indicate their quality. Moreover, water specifications for other applications/usages possess their standards. For example, irrigation water must be neither too saline nor contain toxic materials that can be transferred to plants or soil and thus destroying the ecosystems. Water quality for industrial uses also requires different properties based on the specific industrial processes. Some of the low-priced resources of fresh water, such as ground and surface water, are natural water resources. However, such resources can be polluted by human/industrial activities and other natural processes.
Hence, rapid industrial development has prompted the decay of water quality at a disturbing rate. Furthermore, infrastructures, with the absence of public awareness, and less hygienic qualities, significantly affect the quality of drinking water [1]. In fact, the consequences of polluted drinking water are so dangerous and can badly affect health, the environment, and infrastructures. As per the United Nations (UN) report, about 1.5 million people die each year because of contaminated water-driven diseases. In developing countries, it is announced that 80% of health problems are caused by contaminated water. Five million deaths and 2.5 billion illnesses are reported annually [2]. Such a mortality rate is higher than deaths resulting from accidents, crimes, and terrorist attacks [3].
Therefore, it is very important to suggest new approaches to analyze and, if possible, to predict the water quality (WQ). It is recommended to consider the temporal dimension for forecasting the WQ patterns to ensure the monitoring of the seasonal change of the WQ [4]. However, using a special variation of models together to predict the WQ grants better results than using a single model [5–7]. There are several methodologies proposed for the prediction and modeling of the WQ. These methodologies include statistical approaches, visual modeling, analyzing algorithms, and predictive algorithms. For the sake of the determination of the correlation and relationship among different water quality parameters, multivariate statistical techniques have been employed [4]. The geostatistical approaches were used for transitional probability, multivariate interpolation, and regression analysis [5].
Massive increases in population, the industrial revolution, and the use of fertilizers and pesticides have led to serious effects on the WQ environments [8, 9]. Thus, having models for the prediction of the WQ is of great help for monitoring water contamination.
Currently, two main types for modeling and predicting water quality are available: mechanism- and non-mechanism-oriented models. The mechanism model is relatively sophisticated; it uses the advanced system structure data for simulating the WQ, and thus, it is considered as a multifunctional model that can be used for any water body. In addition, the Streeter–Phelos (S–P) model, one of the earliest WQ simulation model, has been used widely.
Later, some countries have developed a variety of WQ models including the QUAL model [10] and the WASP model [11], which have gained wide usage in mimicking the water quality of rivers. This was followed by Warren and Bach [12] who suggested to use MIKE21 for designing systems to model the estuaries, coastal waters, and seas.
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