Work

Predictive Modeling of Agricultural Water Footprints in Iraq Using Machine Learning

Year: 2025

Type: article

Abstract: The research used machine learning and data mining to improve the use of water in agriculture in Iraq, assessing the water footprints of crops. Datasets from 4TU.ResearchData and Google Earth Engine were merged. The datasets were integrated into classification or regression algorithms to predict either crops locally planted or imported. The algorithms that used SVM and Random Forest achieved high accuracy successfully, which can help with the management of sustainable water and the selection of crops in regions with limited water resources.

Source: Journal of Information Systems Engineering & Management

DOI: 10.52783/jisem.v10i49s.10037

Author: Ali Hasan Taresh

Institution: Thai Nguyen University of Information and Communication Technology

Language: English

Topic: Environmental Impact and Sustainability

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Zero hunger, Clean water and sanitation

Open Access status: diamond

OpenAlex ID: W4410830209

FWCI:

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