Research on dynamic prediction of vegetation coverage by precipitation-evapotranspiration in arid regions based on CNN-LSTM hybrid model
Kai Peng, Yanfei Zhang, Tiejun Liu, Zijing Li, Wentao Liang, Hualin Liu, Yawen Bai · 2025
Read Research on dynamic prediction of vegetation coverage by precipitation-evapotranspiration in arid regions based on CNN-LSTM hybrid model by Kai Peng, Yanfei Zhang, Tiejun Liu, Zijing Li, Wentao Liang, Hualin Liu, Yawen Bai (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12804997.txt.
Description
Kai Peng and six co-authors published an environmental modeling study in twenty twenty-five on forecasting plant cover in drylands from rainfall and evaporation data. In arid regions water rules vegetation, but greenness responds with a lag that simple models miss. The team built a hybrid deep learning model pairing convolutional and memory networks.
A drought index first sorted the study area into aridity types, giving the model varied training cases. The convolutional part reads spatial patterns in the water data; the recurrent part follows long time dependencies. The model distinguished how heavy rain contributes far more to anomalies in arid than semi-arid zones. Dynamic change indices tracked cover over time, supporting land management under a drying climate.
Catalog record
| CREATOR | Kai Peng, Yanfei Zhang, Tiejun Liu, Zijing Li, Wentao Liang, Hualin Liu, Yawen Bai |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 58 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |