LLMs & XAI for Water Sustainability: Seasonal Water Quality Prediction with LIME Explainable AI and a RAG-based Chatbot for Insights

• URL: http://arxiv.org/abs/2409.10898v2
• Date: Thu, 30 Jan 2025 15:47:33 GMT
• Title: LLMs & XAI for Water Sustainability: Seasonal Water Quality Prediction with LIME Explainable AI and a RAG-based Chatbot for Insights
• Authors: Biplov Paneru, Bishwash Paneru,
• Abstract summary: This paper introduces a hybrid deep learning model to predict Nepal's seasonal water quality using a small dataset with multiple water quality parameters. CatBoost, XGBoost, Extra Trees, and LightGBM, along with a neural network combining CNN and RNN layers, are used to capture temporal and spatial patterns in the data. The model demonstrated notable accuracy improvements, aiding proactive water quality control.
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• Abstract: Ensuring safe water supplies requires effective water quality monitoring, especially in developing countries like Nepal, where contamination risks are high. This paper introduces a hybrid deep learning model to predict Nepal's seasonal water quality using a small dataset with multiple water quality parameters. Models such as CatBoost, XGBoost, Extra Trees, and LightGBM, along with a neural network combining CNN and RNN layers, are used to capture temporal and spatial patterns in the data. The model demonstrated notable accuracy improvements, aiding proactive water quality control. CatBoost, XGBoost, and Extra Trees Regressor predicted Water Quality Index (WQI) values with an average RMSE of 1.2 and an R2 score of 0.99. Additionally, classifiers achieved 99 percent accuracy, cross-validated across models. LIME analysis highlighted the importance of indicators like EC and DO levels in XGBoost classification decisions. The neural network model achieved 92 percent classification accuracy and an R2 score of 0.97, with an RMSE of 2.87 in regression analysis. Furthermore, a multifunctional application was developed to predict WQI values using both regression and classification methods.

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