Assessing Meteorological Drought Patterns and Forecasting Accuracy with SPI and SPEI Using Machine Learning Models
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Feng, Puyu & Wang, Bin & Liu, De Li & Yu, Qiang, 2019. "Machine learning-based integration of remotely-sensed drought factors can improve the estimation of agricultural drought in South-Eastern Australia," Agricultural Systems, Elsevier, vol. 173(C), pages 303-316.
- Amin Asadollahi & Binod Ale Magar & Bishal Poudel & Asyeh Sohrabifar & Ajay Kalra, 2024. "Application of Machine Learning Models for Improving Discharge Prediction in Ungauged Watershed: A Case Study in East DuPage, Illinois," Geographies, MDPI, vol. 4(2), pages 1-15, June.
- Chaitanya B. Pande & N. L. Kushwaha & Israel R. Orimoloye & Rohitashw Kumar & Hazem Ghassan Abdo & Abebe Debele Tolche & Ahmed Elbeltagi, 2023. "Comparative Assessment of Improved SVM Method under Different Kernel Functions for Predicting Multi-scale Drought Index," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1367-1399, February.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Wang, Xinzhi & Lin, Qingxia & Wu, Zhiyong & Zhang, Yuliang & Li, Changwen & Liu, Ji & Zhang, Shinan & Li, Songyu, 2025. "Agricultural GDP exposure to drought and its machine learning-based prediction in the Jialing River Basin, China," Agricultural Water Management, Elsevier, vol. 307(C).
- Natalie Teale & David A. Robinson, 2022. "Long-term variability in atmospheric moisture transport and relationship with heavy precipitation in the eastern USA," Climatic Change, Springer, vol. 175(1), pages 1-23, November.
- Md. Monirul Islam & Shusuke Matsushita & Ryozo Noguchi & Tofael Ahamed, 2022. "A damage-based crop insurance system for flash flooding: a satellite remote sensing and econometric approach," Asia-Pacific Journal of Regional Science, Springer, vol. 6(1), pages 47-89, February.
- Israel R. Orimoloye & Adeyemi O. Olusola & Johanes A. Belle & Chaitanya B. Pande & Olusola O. Ololade, 2022. "Drought disaster monitoring and land use dynamics: identification of drought drivers using regression-based algorithms," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 112(2), pages 1085-1106, June.
- Farshad Ahmadi & Saeid Mehdizadeh & Babak Mohammadi, 2021. "Development of Bio-Inspired- and Wavelet-Based Hybrid Models for Reconnaissance Drought Index Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 4127-4147, September.
- Lei Liu & Jianqin Ma & Xiuping Hao & Qingyun Li, 2019. "Limitations of Water Resources to Crop Water Requirement in the Irrigation Districts along the Lower Reach of the Yellow River in China," Sustainability, MDPI, vol. 11(17), pages 1-18, August.
- Mohammed Majeed Hameed & Siti Fatin Mohd Razali & Wan Hanna Melini Wan Mohtar & Norinah Abd Rahman & Zaher Mundher Yaseen, 2023. "Machine learning models development for accurate multi-months ahead drought forecasting: Case study of the Great Lakes, North America," PLOS ONE, Public Library of Science, vol. 18(10), pages 1-37, October.
- Changfu Tong & Hongfei Hou & Hexiang Zheng & Ying Wang & Jin Liu, 2024. "A Coupled Model for Forecasting Spatiotemporal Variability of Regional Drought in the Mu Us Sandy Land Using a Meta-Heuristic Algorithm," Land, MDPI, vol. 13(11), pages 1-22, October.
- Xiao, Xin & Ming, Wenting & Luo, Xuan & Yang, Luyi & Li, Meng & Yang, Pengwu & Ji, Xuan & Li, Yungang, 2024. "Leveraging multisource data for accurate agricultural drought monitoring: A hybrid deep learning model," Agricultural Water Management, Elsevier, vol. 293(C).
- Ji Eun Kim & Jisoo Yu & Jae-Hee Ryu & Joo-Heon Lee & Tae-Woong Kim, 2021. "Assessment of regional drought vulnerability and risk using principal component analysis and a Gaussian mixture model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 109(1), pages 707-724, October.
- Qian Zhu & Yulin Luo & Dongyang Zhou & Yue-Ping Xu & Guoqing Wang & Ye Tian, 2021. "Drought prediction using in situ and remote sensing products with SVM over the Xiang River Basin, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(2), pages 2161-2185, January.
- Endre Harsányi & Bashar Bashir & Firas Alsilibe & Muhammad Farhan Ul Moazzam & Tamás Ratonyi & Abdullah Alsalman & Adrienn Széles & Aniko Nyeki & István Takács & Safwan Mohammed, 2022. "Predicting Modified Fournier Index by Using Artificial Neural Network in Central Europe," IJERPH, MDPI, vol. 19(17), pages 1-19, August.
- Amin Asadollahi & Asyeh Sohrabifar & Habibollah Fakhraei, 2025. "Trihalomethane Formation from Soil-Derived Dissolved Organic Matter During Chlorination and Chloramination: A Case Study in Cedar Lake, Illinois," Geographies, MDPI, vol. 5(1), pages 1-14, March.
- Ruchika Nanwani & Md Mahmudul Hasan & Silvia Cirstea, 2023. "Techniques used to predict climate risks: a brief literature survey," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(2), pages 925-951, September.
- Ning Luo & Qingfeng Meng & Puyu Feng & Ziren Qu & Yonghong Yu & De Li Liu & Christoph Müller & Pu Wang, 2023. "China can be self-sufficient in maize production by 2030 with optimal crop management," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
- Pilar Benito-Verdugo & José Martínez-Fernández & Ángel González-Zamora & Laura Almendra-Martín & Jaime Gaona & Carlos Miguel Herrero-Jiménez, 2023. "Impact of Agricultural Drought on Barley and Wheat Yield: A Comparative Case Study of Spain and Germany," Agriculture, MDPI, vol. 13(11), pages 1-20, November.
- Fengjie Gao & Si Zhang & Rui Yu & Yafang Zhao & Yuxin Chen & Ying Zhang, 2023. "Agricultural Drought Risk Assessment Based on a Comprehensive Model Using Geospatial Techniques in Songnen Plain, China," Land, MDPI, vol. 12(6), pages 1-19, June.
- Xu, Zhenheng & Sun, Hao & Zhang, Tian & Xu, Huanyu & Wu, Dan & Gao, JinHua, 2023. "Evaluating established deep learning methods in constructing integrated remote sensing drought index: A case study in China," Agricultural Water Management, Elsevier, vol. 286(C).
- Cem Polat Cetinkaya & Mert Can Gunacti, 2024. "Meteorological and Agricultural Drought Risk Assessment via Kaplan–Meier Survivability Estimator," Agriculture, MDPI, vol. 14(3), pages 1-15, March.
- Chen, Hao & Yang, Ni & Song, Xuanhua & Lu, Chunhua & Lu, Menglan & Chen, Tan & Deng, Shulin, 2025. "A novel agricultural drought index based on multi-source remote sensing data and interpretable machine learning," Agricultural Water Management, Elsevier, vol. 308(C).
More about this item
Keywords
drought indices; random forest; ANN; SVM; drought prediction;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jforec:v:6:y:2024:i:4:p:51-1044:d:1520848. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.