Performance Evaluation of Deep Learning Models for Water Quality Index Prediction: A Comparative Study of LSTM, TCN, ANN, and MLP

Increased population, urbanization, adoption of modern life styles, and congested population structures pose problems of sewage disposal and pollution of surface waters like lakes. Natural water gets polluted because of weathering of rocks, seepage of soils, and mining processes, etc. [1]. Water quality assessment is used to assess the quality of water based on multiple parameters such as temperature, electrical conductivity, nitrate, phosphorus, potassium, dissolved oxygen, etc. Water Quality Index (WQI) aggregates data from these parameters and produces a single numer that is helpful for the water quality assessment [2]. It facilitates a thorough judgment of water conditions in an environment and directs resource management strategies along with the appropriate treatment plan for it [3-5]. Traditionally, WQI is estimated using different mathematical procedures [6], however, recently, Machine Learning (ML) methods are used for its more feasible and costeffective estimation [7]. Because of their robust nature to handle complex data patterns, these methods have become a viable paradigm of improved predictions.