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Water pollution is a global problem. In developing countries like India, water pollution is growing exponentially due to faster unsustainable industrial developments and poor waste-water management. Hence, it is essential to predict the future levels of pollutants from the historical water quality data of the reservoir with the help of appropriate water quality modeling and forecasting. Subsequently, these forecasting results can be utilized to plan and execute the water quality management steps in advance. This chapter presents a comprehensive review of time series forecasting of the water quality parameters using classical statistical and artificial intelligence-based techniques. Here, important methods used to calculate the water quality index are discussed briefly. Further, a problem formulation for the modeling of water quality parameters, the performance metrics suitable for evaluating the time-series methods, comparative analysis, and important research challenges of the water quality time-series modeling and forecasting are presented.

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