- The inspiration behind the AI-Enabled Water Well Prediction System is to address the critical issue of water scarcity in rural villages, towns, and cities.
- Access to clean and reliable groundwater sources is essential for agriculture, drinking water supply, and industrial activities.
- Traditional methods of well construction often lack precision, leading to inefficient resource utilization.
- This project seeks to leverage AI and machine learning to optimize well construction, ensuring sustainable water access for communities.
- The AI-Enabled Water Well Prediction System is a comprehensive platform that combines data integration, machine learning models, and user-friendly interfaces to facilitate informed decision-making in well construction.
- It predicts key parameters such as well suitability, depth, discharge, drilling techniques, and groundwater quality based on geological and hydrological data.
- The system aims to maximize the success rate of well construction while minimizing resource wastage and environmental impact.
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Data Collection and Integration: Diverse groundwater-related data, including lithology, geophysical logs, water levels, water quality, and aquifer maps, were collected and preprocessed into a unified format for analysis.
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AI Model Development: Machine learning models, including Random Forest, were trained to predict well-related parameters. Fine-tuning and model improvement were achieved through continuous learning from new data and user feedback.
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User-Friendly Interface: An intuitive web-based interface with map-based location selection was developed. AI model predictions are presented in a user-friendly manner, providing valuable insights for well construction decisions.
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Feedback Loop and User Engagement: A structured feedback mechanism was implemented to collect user input on system predictions and usability, enabling continuous improvement.
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Data Privacy and Security: Robust data privacy and security measures were implemented to protect user information and maintain data integrity.
- Successful integration of diverse groundwater-related datasets into a unified platform.
- Development of accurate machine learning models for predicting well parameters.
- Creation of a user-friendly interface that empowers users with valuable insights.
- Establishing a feedback loop for continuous improvement based on user input.
- Through the development process, we gained insights into the importance of feature selection and data quality in training accurate machine learning models.
- Random Forest proved to be a robust model for predicting well-related parameters, providing accurate results across different data types and locations.
- We learned the significance of interpretability in AI models, enabling users to trust and understand the predictions made by the system.
- Expansion: We envision reaching every remote rural village, town, and city with the benefits of AI-optimized well construction.
- Enhanced Predictions: Continuous model refinement and the incorporation of real-time data will further improve prediction accuracy.
- Mobile Application: Developing a mobile app for on-field well construction teams to access predictions and guidance in real-time.
- Community Engagement: Collaborating with local communities and stakeholders to ensure sustainable well construction practices and water resource management.
- Data Integration: Harmonizing and preprocessing diverse datasets proved challenging due to variations in data quality and formats.
- Model Generalization: Ensuring that machine learning models generalize well across different geographical regions and geological conditions.
- User Adoption: Convincing users to trust AI predictions and actively participate in the feedback loop was a challenge.
- Our mission is to use AI to enhance existing technologies and practices related to well prediction locator and contruction, ensuring sustainable access to groundwater for communities worldwide.
- We aim to revolutionize the well construction process, making it more efficient, cost-effective, and environmentally friendly, ultimately contributing to global water resource sustainability.
- Numpy
- Pandas
- Matplotlib
- Joblib
- Scikit Learn
- Streamlit
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Github :https://github.com/pawan-cpu/SIH-AI-enabled-Water-Well-Prediction-Model
https://water-well-predictor-4gtfeehgyxsxkbzxwtd9kf.streamlit.app/