Combination of data-driven models and best subset regression for predicting the standardized precipitation index (SPI) at the Upper Godavari Basin in India
Standardized precipitation index prediction and monitoring are essential to mitigating the effect of drought actions on precision farming, environments, climate-smart agriculture, and the water cycle. In this study, four data-driven models, additive regression, random subspace, M5Pruned (M5P), and b...
保存先:
主要な著者: | Pande C.B., Costache R., Sammen S.S., Noor R., Elbeltagi A. |
---|---|
その他の著者: | 57193547008 |
フォーマット: | 論文 |
出版事項: |
Springer
2024
|
主題: | |
タグ: |
タグ追加
タグなし, このレコードへの初めてのタグを付けませんか!
|
類似資料
-
Extreme heat vulnerability assessment in Peninsular Malaysia with integration of remote sensing and sociodemographic data
著者:: Ahmad Kamal, Nurfatin Izzati
出版事項: (2021) -
Comparative Assessment of Improved SVM Method under Different Kernel Functions for Predicting Multi-scale Drought Index
著者:: Pande C.B., 等
出版事項: (2024) -
Prediction of meteorological drought and standardized precipitation index based on the random forest (RF), random tree (RT), and Gaussian process regression (GPR) models
著者:: Elbeltagi A., 等
出版事項: (2024) -
Forecasting of meteorological drought using ensemble and machine learning models
著者:: Pande C.B., 等
出版事項: (2025) -
Evaluating the groundwater recharge requirement and restoration in the Kanari river, India, using SWAT model
著者:: Trivedi A., 等
出版事項: (2025)