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Scientometric Analysis-Based Review for Drought Modelling, Indices, Types, and Forecasting Especially in Asia
An extended drought period with low precipitation can result in low water availability and issues for humans, animals, and plants. Drought forecasting is critical for water resource development and management as it helps to reduce negative consequences. In this study, scientometric analysis and manual comprehensive analysis on drought modelling and forecasting are used. A scientometric analysis is used to determine the current research trend using bibliometric data and to identify relevant publication field sources with the most publications, the most frequently used keywords, the most cited articles and authors, and the countries that have made the greatest contributions to the field of water resources. This paper also tries to provide an overview of water issues, such as drought classification, drought indices, historical droughts, and their impact on Asian countries such as China, Pakistan, India, and Iran. There have been many models established for this purpose and choosing the appropriate model for study is a long procedure for researchers. An appropriate, comprehensive, pedagogical study of model ideas and historical implementations would benefit researchers by helping them to avoid overlooking viable model options, thus reducing their time spent on the topic. As a result, the goal of this paper is to review drought-forecasting approaches and recommend the best models for the Asian region. The models are divided into four categories based on their mechanisms: Regression analysis, stochastic modelling, machine learning, and dynamic modelling. The basic concepts of each approach in terms of the model’s historical use, benefits, and limitations are explained. Finally, prospects for future drought research in Asia are discussed as well as potential modelling techniques.
Scientometric Analysis-Based Review for Drought Modelling, Indices, Types, and Forecasting Especially in Asia
An extended drought period with low precipitation can result in low water availability and issues for humans, animals, and plants. Drought forecasting is critical for water resource development and management as it helps to reduce negative consequences. In this study, scientometric analysis and manual comprehensive analysis on drought modelling and forecasting are used. A scientometric analysis is used to determine the current research trend using bibliometric data and to identify relevant publication field sources with the most publications, the most frequently used keywords, the most cited articles and authors, and the countries that have made the greatest contributions to the field of water resources. This paper also tries to provide an overview of water issues, such as drought classification, drought indices, historical droughts, and their impact on Asian countries such as China, Pakistan, India, and Iran. There have been many models established for this purpose and choosing the appropriate model for study is a long procedure for researchers. An appropriate, comprehensive, pedagogical study of model ideas and historical implementations would benefit researchers by helping them to avoid overlooking viable model options, thus reducing their time spent on the topic. As a result, the goal of this paper is to review drought-forecasting approaches and recommend the best models for the Asian region. The models are divided into four categories based on their mechanisms: Regression analysis, stochastic modelling, machine learning, and dynamic modelling. The basic concepts of each approach in terms of the model’s historical use, benefits, and limitations are explained. Finally, prospects for future drought research in Asia are discussed as well as potential modelling techniques.
Scientometric Analysis-Based Review for Drought Modelling, Indices, Types, and Forecasting Especially in Asia
Dan Wu (author) / Yanan Li (author) / Hui Kong (author) / Tingting Meng (author) / Zenghui Sun (author) / Han Gao (author)
2021
Article (Journal)
Electronic Resource
Unknown
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