Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Efficient water pump management is critical for
ensuring a steady and reliable water supply, especially in
regions where demand fluctuates due to environmental,
agricultural, and industrial factors. Our project focuses on
developing a demand forecasting model for water pumps, using
historical data and predictive analytics. By analyzing key
variables such as weather patterns, population growth, and
seasonal demand variations, the model aims to provide
accurate short- and long-term forecasts. Various machine
learning algorithms, including time series analysis and
regression models, are employed to enhance prediction
accuracy. The implementation of this model can help optimize
resource allocation, reduce operational costs, and improve
water pump distribution planning. Results indicate that the
forecasting model offers significant improvements in
predicting demand trends, contributing to more efficient water
resource management.
Keywords:
Water pump management, demand forecasting, predictive analytics, machine learning, time series analysis, regression models, weather patterns, population growth.
Cite Article:
"ForeCasting Demand For Waterpump", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.a585-a589, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505068.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator