Water Quality Prediction Using Combined Model of Convolutional Neural Network and Long Short-Term Memory
Keywords:
Classification, Combined model, Dynamic particle swarm optimization, Prediction, Water qualityAbstract
In recent decades, the quality of water is affected due to contamination and pollution of water bodies. The existing techniques face issues related to poor water quality prediction with less accuracy. This research focusses on an effective water quality classification framework by predicting it as safe or unsafe. Initially, the data is acquisitioned from Kaggle and it is subjected to the stage of pre-processing using standard scalar. The pre-processed output is provided for feature selection takes place using dynamic Particle Swarm Optimization (PSO). After this, the classification is performed using combined method of Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM). The CNN acts as front end of model which processes the input features based on non-linear characteristics and LSTM acts as the back end which receives the abstracted data that helps in predicting the water quality as safe or unsafe. The outcome through the experimental validation shows that the suggested framework achieves prediction accuracy of 99.99% which is comparably higher than ensemble model with classification accuracy of 98.1%.
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Link for dataset: https://www.kaggle.com/datasets/mssmartypants/water-quality
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