Examining the Impacts of Recent Water Availability on the Future Food Security Risks in Pakistan Using Machine Learning Approaches

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成果类型：

期刊论文

作者：

Shah, Wilayat;Chen, Junfei;Ullah, Irfan;Shah, Ashfaq Ahmad;Alotaibi, Bader Alhafi;...

通讯作者：

Shah, AA

作者机构：

[Shah, Wilayat; Chen, Junfei] Hohai Univ, Business Sch, Nanjing 210098, Peoples R China.

[Ullah, Irfan] Hohai Univ, Coll Hydrol & Water Resources, Nanjing 210098, Peoples R China.

[Shah, Ashfaq Ahmad; Shah, AA] China Agr Univ, Coll Humanities & Dev Studies, Beijing 100193, Peoples R China.

[Alotaibi, Bader Alhafi] King Saud Univ, Coll Food & Agr Sci, Dept Agr Extens & Rural Soc, Riyadh 11451, Saudi Arabia.

[Syed, Sidra] Nanjing Univ Informat Sci & Technol, Key Lab Meteorol Disaster, Minist Educ KLME,Collaborat Innovat Ctr Forecast &, Joint Int Res Lab Climate & Environm Change ILCEC, Nanjing 211544, Peoples R China.

通讯机构：

[Shah, AA ] C

China Agr Univ, Coll Humanities & Dev Studies, Beijing 100193, Peoples R China.

语种：

英文

关键词：

machine learning;water availability;food security;risk;Pakistan

期刊：

Water

ISSN：

2073-4441

年：

2025

卷：

期：

页码：

55-

DOI：

10.3390/w17010055

基金类别：

Researchers Supporting [RSP2024R443]; King Saud University, Riyadh, Saudi Arabia - National Natural Science Foundation of China for International Scientists [42350410438]

机构署名：

本校为其他机构

院系归属：

班戈学院

摘要：

Food and water security are critical challenges in Pakistan, exacerbated by rapid population growth, climate variability, and limited resources. This study explores the application of machine learning techniques to address these issues. We specifically examine the dimensions of food and water security in Pakistan, employing data-driven methods to enhance crop yield predictions, food production forecasting, and water resource management. Using secondary data, we refine machine learning models, such as random forest and linear regression, to analyze water availability, crop yield, and crop produ...

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Examining the Impacts of Recent Water Availability on the Future Food Security Risks in Pakistan Using Machine Learning Approaches

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