An effective illustrative approach for water quality prediction using spatio temporal features with cross attention-based adaptive long short-term memory
Restricted accessResearch articleFirst published online March, 2025
An effective illustrative approach for water quality prediction using spatio temporal features with cross attention-based adaptive long short-term memory
Water quality prediction is significant for handling water resources for ensuring the quality of drinking water. Forecasting the chemical parameters present in water at a specific point is necessary for predicting the quality of water. Uneven observation results in missing information, which makes it challenging to make exact forecasts. Conventional techniques for predicting water quality are quite straightforward and linear models, which are frequently employed. Nevertheless, these models have restrictions in terms of forecasting the nonlinear features of water quality. Due to the intricate water environment, the water quality time series is significant in this work. In this article, effective water quality prediction using a spatio-temporal features model is developed and implemented. At first, the requisite data will be fetched from the standard online resources. After that, the attained data are given to the Spatio-Temporal Features with Cross Attention-based Adaptive Long Short Term Memory (SRF-CAALSTM) technique, where the quality of the water is predicted. In order to enrich the system functionality the parameters of the LSTM model is tuned with the assistance of the Improved Osprey Optimization Algorithm (IOOA). Thus, effective predicted outcomes are attained for the water quality. In the end, extensive analyses are carried out to validate and contrast the suggested prediction model with the conventional mechanisms. This assured the effectiveness of the recommended water quality prediction approach.
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