Metaheuristic-enhanced deep learning for monthly pan evaporation prediction under limited climatic data
Ozgur Kisi, Rana Muhammad Adnan, Mohammad Zounemat-Kermani, Saad Sh. Sammen, Adil Masood, Christoph Külls · 2026
Read Metaheuristic-enhanced deep learning for monthly pan evaporation prediction under limited climatic data by Ozgur Kisi, Rana Muhammad Adnan, Mohammad Zounemat-Kermani, Saad Sh. Sammen, Adil Masood, Christoph Külls (2026) 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-13135511.txt.
Description
Ozgur Kisi and co-authors improve in 2026 forecasts of monthly water loss from an open pan in southeast China, using only limited weather data. They add two recent nature inspired search methods to a memory based deep network, to tune its settings. The study uses forty years of data from two stations and tests three ways of splitting the data.
Both new hybrids beat a plain network and two older hybrids. The best model cut key error scores by more than forty percent against the best plain network in the top test case. Charts show its forecasts track the observed values closely. The authors argue that smart tuning greatly lifts such forecasts.
Catalog record
| CREATOR | Ozgur Kisi, Rana Muhammad Adnan, Mohammad Zounemat-Kermani, Saad Sh. Sammen, Adil Masood, Christoph Külls |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 86 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |