ISSN2320-2882
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Paper Title
EcoForecast AI: Anticipating Wet and Dry Waste Generation Using Predictive Analytics
Authors
M.Pratibha , D.Ashwini , M.Bhagya Rekha
Keywords
Keywords: Predictive analytics, Artificial Intelligence, Waste management, Wet waste, Dry waste, Smart cities, Power BI, Data visualization, Decision support systems, Sustainable urban planning.
Abstract
Abstract
Rapid urbanization and population growth have significantly increased the volume of municipal solid waste, placing pressure on existing waste management systems [1], [13]. Efficient segregation and timely collection of wet and dry waste are essential for sustainable urban development. However, conventional waste management practices rely heavily on historical averages and manual estimation, leading to inefficiencies in collection planning and resource utilization.
This paper proposes EcoForecast AI, an Artificial Intelligence-based predictive analytics framework designed to anticipate wet and dry waste generation patterns. The framework analyzes historical waste data, seasonal variations, population dynamics, and consumption behavior to forecast future waste quantities. By integrating machine learning models with predictive analytics, the proposed system enables proactive waste collection planning, optimized resource allocation, and improved segregation strategies.
Furthermore, Power BI is utilized as a dynamic visualization and reporting tool within the framework. By creating interactive dashboards, Power BI allows municipal authorities to monitor real-time waste generation trends, identify high-risk areas, and evaluate the effectiveness of collection strategies. The visualizations also support scenario analysis, enabling stakeholders to simulate the impact of policy changes, population growth, or seasonal variations on waste generation. The combination of AI-based forecasting with Power BI's intuitive dashboards enhances transparency, informed decision-making, and citizen engagement in smart waste management initiatives.
Experimental evaluation using simulated municipal datasets demonstrates improved prediction accuracy, reduced forecasting error, and enhanced decision-making capabilities.
EcoForecast AI, complemented by Power BI's visualization capabilities, offers a scalable and intelligent solution for smart and sustainable urban waste management systems.
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IJCRT — journal style
"EcoForecast AI: Anticipating Wet and Dry Waste Generation Using Predictive Analytics", IJCRT - International Journal of Creative Research Thoughts (www.ijcrt.org), ISSN:2320-2882, Vol.14, Issue 2, page no.b136-b144, February 2026, Available :https://ijcrt.org/papers/IJCRT2602127.pdf
APA — 7th edition
M.Pratibha, D.Ashwini, & Rekha, M. B. (2026). EcoForecast AI: Anticipating Wet and Dry Waste Generation Using Predictive Analytics. International Journal of Creative Research Thoughts, 14(2), b136-b144. https://doi.org/10.56975/ijcrt.v14i2.301201
MLA — 9th edition
M.Pratibha, et al. "EcoForecast AI: Anticipating Wet and Dry Waste Generation Using Predictive Analytics." International Journal of Creative Research Thoughts, vol. 14, no. 2, 2026, pp. b136-b144, https://doi.org/10.56975/ijcrt.v14i2.301201.
Chicago — 17th, bibliography
M.Pratibha, D.Ashwini, and M.Bhagya Rekha. "EcoForecast AI: Anticipating Wet and Dry Waste Generation Using Predictive Analytics." International Journal of Creative Research Thoughts 14, no. 2 (2026): b136-b144. https://doi.org/10.56975/ijcrt.v14i2.301201.
Harvard — author–date
M.Pratibha, D.Ashwini and Rekha, M.B. (2026) 'EcoForecast AI: Anticipating Wet and Dry Waste Generation Using Predictive Analytics', International Journal of Creative Research Thoughts, 14(2), pp. b136-b144. Available at: https://doi.org/10.56975/ijcrt.v14i2.301201
IEEE — numbered reference
M.Pratibha, D.Ashwini and M. B. Rekha, "EcoForecast AI: Anticipating Wet and Dry Waste Generation Using Predictive Analytics," IJCRT, vol. 14, no. 2, pp. b136-b144, Feb. 2026, doi: 10.56975/ijcrt.v14i2.301201.
Vancouver — biomedical
M.Pratibha, D.Ashwini, Rekha MB. EcoForecast AI: Anticipating Wet and Dry Waste Generation Using Predictive Analytics. IJCRT. 2026 Feb;14(2):b136-b144. doi: 10.56975/ijcrt.v14i2.301201
AMA — 11th edition
M.Pratibha, D.Ashwini, Rekha MB. EcoForecast AI: Anticipating Wet and Dry Waste Generation Using Predictive Analytics. IJCRT. 2026;14(2):b136-b144. doi:10.56975/ijcrt.v14i2.301201
BibTeX — Zotero, Mendeley, LaTeX
@article{IJCRT2602127,
author = {M.Pratibha and D.Ashwini and M.Bhagya Rekha},
title = {EcoForecast AI: Anticipating Wet and Dry Waste Generation Using Predictive Analytics},
journal = {International Journal of Creative Research Thoughts},
volume = {14},
number = {2},
pages = {b136-b144},
year = {2026},
month = {February},
issn = {2320-2882},
doi = {10.56975/ijcrt.v14i2.301201},
url = {https://doi.org/10.56975/ijcrt.v14i2.301201}
}
RIS — EndNote, RefWorks
TY - JOUR
AU - M.Pratibha
AU - D.Ashwini
AU - Rekha, M.Bhagya
TI - EcoForecast AI: Anticipating Wet and Dry Waste Generation Using Predictive Analytics
T2 - International Journal of Creative Research Thoughts
JA - IJCRT
VL - 14
IS - 2
PY - 2026
SN - 2320-2882
DO - 10.56975/ijcrt.v14i2.301201
UR - https://doi.org/10.56975/ijcrt.v14i2.301201
SP - b136-b144
ER -
Issue
Volume 14 Issue 2,
February-2026
Pages : b136-b144
Article Information
- Published In
- International Journal of Creative Research Thoughts — Volume 14, Issue 2, February 2026
- Pages
- b136-b144
- Published
- February 2026
- Registration ID
- IJCRT_301201
- Published Paper ID
- IJCRT2602127
- Research Area
- Science and Technology
- Author Type
- Indian Author
- Location
- secundrabad, telagana, India
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- Request Peer Review Report
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- CC BY 4.0 · Open Access
- ISSN & Impact Factor
- 2320-2882 · IF 9.17
- Downloads
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- Published Paper PDF
- https://ijcrt.org/papers/IJCRT2602127.pdf
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