Multitemporal meteorological drought forecasting using Bat-ELM

Gholizadeh, Reza; Yılmaz, Hasan; Danandeh Mehr, Ali

doi:10.1007/s11600-022-00739-1

Artykuł - szczegóły

Tytuł artykułu

Multitemporal meteorological drought forecasting using Bat-ELM

Autorzy

Gholizadeh Reza , Yılmaz Hasan , Danandeh Mehr Ali

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-022-00739-1

Warianty tytułu

Języki publikacji

Abstrakty

The advancement of the machine learning (ML) models has demonstrated notable progress in geosciences. They can identify the underlying process or causality of natural hazards. This article introduces the development and verification procedures of a new hybrid ML model, namely Bat-ELM for predictive drought modelling. The multi-temporal standardized precipitation evapotranspiration index (SPEI-3 and SPEI-6) is computed as the meteorological drought index at two study regions (Beypazari and Nallihan), located in Ankara province, Turkey. The proposed hybrid model is obtained by integrating the Bat optimization algorithm as the parameter optimizer with an extreme learning machine (ELM) as the regressor engine. The efficiency of the intended model was evaluated against the classic artificial neural network (ANN) and standalone ELM models. The evaluation and assessment are conducted using statistical metrics and graphical diagrams. The forecasting results showed that the accuracy of the proposed model outperformed the benchmark models. In a quantitative assessment, the Bat-ELM model attained minimal root mean square error for the SPEI-3 and SPEI-6 (RMSE=0.58 and 0.43 at Beypazari station and RMSE=0.53 and 0.37 at Nallihan station) over the testing phase. This indicates the new model approximately 20 and 15% improves the forecasting accuracy of traditional ANN and classic ELM techniques, respectively.

Słowa kluczowe

extreme learning machine drought forecasting hydroclimatology bat algorithm SPEI

maszyna do ekstremalnego uczenia się prognozowanie suszy hydroklimatologia algorytm nietoperza SPEI

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2022

Tom

Vol. 70, no 2

Strony

917--927

Opis fizyczny

Bibliogr. 57 poz.

Twórcy

autor

Gholizadeh Reza

reza.gholizadeh11@ogr.atauni.edu.tr

Department of Landscape Architecture, Faculty of Architecture and Design, Ataturk University, Erzurum, Turkey

https://orcid.org/0000-0002-7644-3583

autor

Yılmaz Hasan

hasan_yilmaz@manas.edu.kg

Department of Landscape Architecture, Faculty of Architecture and Design, Ataturk University, Erzurum, Turkey
Faculty of Agriculture, Kyrgyzstan Turkey Manas University, Bishkek, Kyrgyzstan

autor

Danandeh Mehr Ali

Civil Engineering Department, Antalya Bilim University, Antalya, Turkey

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-fac57351-daec-4a36-8b2c-b184592a4c17