ISSN2320-2882
Impact Factor9.17
UGC ParametersAligned
Low Cost₹1501 / $59
CrossRefDOI
ESTD2013
Peer ReviewTransparent
AccessOpen
Submission: Always Open
Paper Title
Water Crisis Prediction Using Machine Learning
Authors
Kanishka C , Sastika S , Sivapriya S , Yuvarani S K , Dr.SHENBAGAVALLI P
Keywords
Water Demand Forecasting, Random Forest Regression, Environmental Data Analytics, Population Growth Modeling, Data-driven Resource Allocation , Feature Engineering for Water Demand, Predictive Dashboard Visualization
Abstract
Efficient water resource management is essential for sustainable urban development, particularly in rapidly growing cities where demand patterns are influenced by environmental and demographic factors. This paper presents a data-driven water demand prediction framework using Random Forest regression to forecast future water consumption. The proposed system integrates historical water usage data, environmental parameters such as rainfall and temperature, reservoir storage levels, and population growth information obtained through external APIs.
The framework consists of data collection, preprocessing, feature engineering, model training, prediction, and visualization modules. Time-series features and lag-based variables are generated to capture seasonal and temporal consumption patterns. The Random Forest regression model is employed due to its ability to handle nonlinear relationships, reduce overfitting through ensemble learning, and provide feature importance insights.
Experimental evaluation demonstrates improved prediction accuracy compared to traditional statistical approaches. The predicted outputs are presented through an interactive dashboard to support water distribution planning and resource allocation. The proposed system provides a scalable and adaptive solution for smart water management, enabling authorities to make data-driven decisions for efficient and sustainable water supply management.
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IJCRT — journal style
"Water Crisis Prediction Using Machine Learning", IJCRT - International Journal of Creative Research Thoughts (www.ijcrt.org), ISSN:2320-2882, Vol.14, Issue 2, page no.e146-e157, February 2026, Available :https://ijcrt.org/papers/IJCRT2602484.pdf
APA — 7th edition
C, K., S, S., S, S., K, Y. S., & P, D. S. (2026). Water Crisis Prediction Using Machine Learning. International Journal of Creative Research Thoughts, 14(2), e146-e157. https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602484
MLA — 9th edition
C, Kanishka, et al. "Water Crisis Prediction Using Machine Learning." International Journal of Creative Research Thoughts, vol. 14, no. 2, 2026, pp. e146-e157, https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602484.
Chicago — 17th, bibliography
C, Kanishka, et al. "Water Crisis Prediction Using Machine Learning." International Journal of Creative Research Thoughts 14, no. 2 (2026): e146-e157. https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602484.
Harvard — author–date
C, K. et al. (2026) 'Water Crisis Prediction Using Machine Learning', International Journal of Creative Research Thoughts, 14(2), pp. e146-e157. Available at: https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602484
IEEE — numbered reference
K. C, S. S, S. S, Y. S. K and D. S. P, "Water Crisis Prediction Using Machine Learning," IJCRT, vol. 14, no. 2, pp. e146-e157, Feb. 2026.
Vancouver — biomedical
C K, S S, S S, K YS, P DS. Water Crisis Prediction Using Machine Learning. IJCRT. 2026 Feb;14(2):e146-e157.
AMA — 11th edition
C K, S S, S S, K YS, P DS. Water Crisis Prediction Using Machine Learning. IJCRT. 2026;14(2):e146-e157. Accessed at https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602484
BibTeX — Zotero, Mendeley, LaTeX
@article{IJCRT2602484,
author = {Kanishka C and Sastika S and Sivapriya S and Yuvarani S K and Dr.SHENBAGAVALLI P},
title = {Water Crisis Prediction Using Machine Learning},
journal = {International Journal of Creative Research Thoughts},
volume = {14},
number = {2},
pages = {e146-e157},
year = {2026},
month = {February},
issn = {2320-2882},
url = {https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602484}
}
RIS — EndNote, RefWorks
TY - JOUR
AU - C, Kanishka
AU - S, Sastika
AU - S, Sivapriya
AU - K, Yuvarani S
AU - P, Dr.SHENBAGAVALLI
TI - Water Crisis Prediction Using Machine Learning
T2 - International Journal of Creative Research Thoughts
JA - IJCRT
VL - 14
IS - 2
PY - 2026
SN - 2320-2882
UR - https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602484
SP - e146-e157
ER -
Issue
Volume 14 Issue 2,
February-2026
Pages : e146-e157
Article Information
- Published In
- International Journal of Creative Research Thoughts — Volume 14, Issue 2, February 2026
- Pages
- e146-e157
- Published
- February 2026
- Registration ID
- IJCRT_301700
- Published Paper ID
- IJCRT2602484
- Research Area
- Science and Technology
- Author Type
- Indian Author
- Location
- Coimbatore, Tamil Nadu, India
- Peer Review
- Request Peer Review Report
- Licence
- CC BY 4.0 · Open Access
- ISSN & Impact Factor
- 2320-2882 · IF 9.17
- Downloads
- 147
- Permanent Link
- https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602484
- Published Paper PDF
- https://ijcrt.org/papers/IJCRT2602484.pdf
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