# An interpretable machine learning approach based on DNN, SVR, Extra Tree, and XGBoost models for predicting daily pan evaporation

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/an-interpretable-machine-learning-approach-based-on-dnn-svr-extra-tree-and/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Ali El Bilali,Taleb Abdeslam,Ayoub Nafii,Houda Lamane,Mohamed Abdellah Ezzaouini,Ahmed Elbeltagi |
| Citations | 196 |
| DOI | 10.1016/j.jenvman.2022.116890 |
| Fields | Earth and Planetary Sciences,Environmental Science |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W4310346536 |
| PMID | 36459782 |
| Type | article |
| Year | 2022 |

## Paper authors

- [Ahmed Elbeltagi](https://scholariq.org/researchers/ahmed-elbeltagi/)

## Paper primary topic

- [Hydrological Forecasting Using AI](https://scholariq.org/topics/hydrological-forecasting-using-ai/)

## Paper topics

- [Hydrological Forecasting Using AI](https://scholariq.org/topics/hydrological-forecasting-using-ai/)
- [Meteorological Phenomena and Simulations](https://scholariq.org/topics/meteorological-phenomena-and-simulations/)
- [Hydrology and Watershed Management Studies](https://scholariq.org/topics/hydrology-and-watershed-management-studies/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.