Summary of Llms & Xai For Water Sustainability: Seasonal Water Quality Prediction with Lime Explainable Ai and a Rag-based Chatbot For Insights, by Biplov Paneru et al.
LLMs & XAI for Water Sustainability: Seasonal Water Quality Prediction with LIME Explainable AI and a RAG-based Chatbot for Insights
by Biplov Paneru, Bishwash Paneru
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed hybrid deep learning model predicts Nepal’s seasonal water quality using a small dataset with multiple parameters, improving accuracy for proactive control. The model combines CNN and RNN layers, leveraging temporal and spatial patterns in the data. Models like CatBoost, XGBoost, Extra Trees Regressor, LightGBM, and neural networks are used to capture these patterns. Notable improvements were achieved, particularly with CatBoost, XGBoost, and Extra Trees Regressor predicting Water Quality Index (WQI) values with an average RMSE of 1.2 and R2 score of 0.99. Classifiers also reached 99% accuracy, cross-validated across models. LIME analysis highlighted the importance of indicators like EC and DO levels in XGBoost classification decisions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a special computer model that can predict if the water in Nepal is safe or not based on some measurements. They used a lot of data from different times and places to train the model, which got better and better at making predictions. The model was tested and worked really well, especially when it came to predicting things like how clean or dirty the water was. This could be super helpful for people in Nepal who need to know if their water is safe to drink. |
Keywords
» Artificial intelligence » Classification » Cnn » Deep learning » Rnn » Xgboost
