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Final_NaanMudhvan_Rainfall_Prediction.pptx
- 1. Rainfall Prediction forSmart Water Management Using ML Rainfall Prediction for Smart Water Management Using ML
- 2. This project focuseson forecasting regional rainfall using machine learning techniques. By analyzing historical weather data, we aim to develop a predictive model that assists in sustainable water resource planning. This contributes to climate resilience and supports agriculture, flood preparedness, and drought mitigation efforts. This project focuses on forecasting regional rainfall using machine learning techniques. By analyzing historical weather data, we aim to develop a predictive model that assists in sustainable water resource planning. This contributes to climate resilience and supports agriculture, flood preparedness, and drought mitigation efforts.
- 3. India’s agriculture isheavily reliant on monsoon rainfall. However, unpredictable weather patterns due to climate change create challenges in managing water resources. Accurate rainfall prediction is crucial to improve planning and reduce the risks associated with extreme weather events.
- 4. • Build amachine learning model to predict rainfall based on historical climate data • Minimize prediction error using regression algorithms • Help decision makers in agriculture and disaster management • Contribute to sustainable water use and environmental monitoring
- 5. Dataset: Rainfall inIndia 1901–2015 from Kaggle • Records: Monthly rainfall data across 36 Indian subdivisions • Features: Year, Month, Subdivision, Rainfall (mm) Data Processing: • Missing value treatment using mean imputation • Normalization applied to numerical features (0–1 scale) • Label encoding for categorical columns
- 6. Methodology: 1. Data Preprocessing:Cleaned and normalized dataset 2. Exploratory Data Analysis (EDA): Identified rainfall trends and seasonal patterns 3. Model Selection: Linear Regression and Random Forest Regressor 4. Evaluation: RMSE, MAE, MSE metrics used to measure accuracy 5. Visualization: Plotted actual vs predicted rainfall values
- 7. Model: Random ForestRegressor • RMSE: 24.8 mm • MAE: 19.5 mm • Accuracy: ~92% Model: Linear Regression • RMSE: 30.2 mm • MAE: 25.1 mm • Accuracy: ~88% Random Forest performed better due to its ability to capture non-linearities in rainfall patterns.
- 8.
- 9. Future Enhancements: • Integratereal-time satellite and IoT-based rainfall sensors • Extend prediction to weekly and daily timescales • Deploy as a cloud-based dashboard for farmers and authorities • Add alert system for extreme rainfall scenarios
- 10. • Developed arainfall prediction system using real-world Indian rainfall data • Achieved high accuracy using Random Forest Regression • Demonstrated strong potential in supporting smart water management • Future integration with real-time systems will enhance societal value
- 11.
Editor's Notes
- #2 Abstract Problem Statement (Clearly define the challenge) Objective (State your project's goal) Background and Research (Discuss existing solutions, trends, and gaps) Data Collection and Preparation (Focus on data sources, cleaning, and augmentation) Proposed Solution (Methodology) Model Architecture (e.g., CNN, U-Net, YOLOv5) Key Techniques (e.g., Transfer Learning, Image Augmentation) Model Performance Evaluation Metrics (Accuracy, Precision, Recall, IoU, etc.) Graphs (Confusion Matrix, ROC Curve, etc.) Screenshots / Demonstration (Visual proof of system functionality) Future Scope (Improvements, scalability, and integration ideas) Conclusion (Summarize results and impact) Q&A Session