• Nilesh Lahanu Dalvi
• Amit Admane
• Mothabhau Borkar
• Namrata Ghuse

An accurate prediction of water quality (WQ) related parameter is considered as pivotal decisive tool in sustainable water resources management. For this aim, monthly average data of water temperature (T), Turbidity, pH, Electrical Conductivity (EC), Alkalinity (Alk), Calcium (Ca), chemical oxygen demand (COD), Sulfate (SO4), total dissolved solids (TDS), total suspended solids (TSS), and BOD measured for ten years period were used in this study. Firstly, considering that water quality parameter changes have obvious time characteristics, a time series prediction model of water quality parameters is established based on LSTM. The water quality is predicted in this paper. It is found that it is important to keep the water level in the reservoir at a relative high level. Finally, the dataset we are using is numerical type excel sheet dataset which have data of water quality parameters such as ph , hardness, sulphate, etc. The dataset is collected from the Kaggle. The Programming language we are using is Python. The whole Project is based on window application, so we are using Anaconda software for coding via Spider editor. To record all the registrations and data of user we are using Sq lite software.

Key Words: Water quality, Modelling, Intelligent water service, Support Vector machine.

"Water Quality Prediction Using Artificial Intelligence Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.k323-k327, May-2023, Available :http://www.jetir.org/papers/JETIR2305A44.pdf

"Water Quality Prediction Using Artificial Intelligence Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppk323-k327, May-2023, Available at : http://www.jetir.org/papers/JETIR2305A44.pdf