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@article{192543,
        author = {Raj Suryavanshi and Krunal Bhongle},
        title = {Optimal Water Usage Prediction for Farmers},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {4554-4560},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192543},
        abstract = {The Optimal Water Usage Prediction For Farming project focuses on developing a machine-learning-based system to analyse historical water consumption data and predict future water usage patterns. Efficient water management has become a critical challenge due to increasing population, urbanization, climate variability, and limited freshwater resources. This project addresses the need for proactive planning by leveraging predictive analytics to estimate water demand and support optimized usage strategies.
The system processes past water usage records and relevant influencing factors such as time-based trends, seasonal variations, and usage patterns to train a predictive model capable of forecasting future consumption levels. By identifying patterns and correlations in the data, the model provides accurate predictions that can assist individuals, organizations, or authorities in making informed decisions regarding water allocation, conservation planning, and demand management.
The application is deployed as an interactive web interface using modern machine-learning deployment tools, allowing users to input parameters and instantly obtain water usage predictions. This enhances accessibility and demonstrates the practical applicability of machine learning in real-world environmental and resource management scenarios. Overall, the project showcases how data-driven approaches can contribute to sustainable water usage, reduced wastage, and improved long-term resource planning through intelligent prediction and analysis.},
        keywords = {Water Consumption Forecasting, Predictive Modelling, Machine Learning Algorithms, Time-Series Forecasting, Data Analytics, Sustainable Resource Management, Smart Water Management, Demand Optimization, Environmental Analytics, Decision Support Systems, Consumption Pattern Analysis, Data-Driven Forecasting, Resource Planning, Utility Management, Statistical Learning, Sustainability Analytics, Real-Time Prediction, Intelligent Systems},
        month = {February},
        }