Prediction of Precipitation using Multiscale Geographically Weighted Regression

Yıl 2024, Cilt: 11 Sayı: 2, 61 - 66, 16.06.2024

Murat Taşyürek Mete Çelik Ali Ümran Kömüşcü Filiz Dadaser-celik

https://doi.org/10.30897/ijegeo.1399172

Öz

Prediction of precipitation at locations which lack meteorological measurements is a challenging task in hydrological applications. In this study we aimed to demonstrate potential use of multiscale geographically weighted regression (MGWR) method used to predict precipitation based on relevant meteorological parameters. Geographically weighted regression (GWR) is a regression technique proposed to explore spatial non-stationary relationships. Compared to the linear regression technique, GWR considers the dynamics of local behaviour and, therefore provides an improved representation of spatial variations in relationships. Multiscale geographically weighted regression (MGWR) is a modified version of GWR that examines multiscale processes by providing a scalable and flexible framework. In this study, the MGWR model was used to predict precipitation, which is an essential problem not only in meteorology and climatology, but also in many other disciplines, such as geography and ecology. A meteorological dataset including elevation, precipitation, air temperature, air pressure, relative humidity, and cloud cover data belonging to Türkiye was used, and the performance of the MGWR was assessed in comparison with that of global regression and classical GWR. Experimental evaluations demonstrated that the MGWR model outperformed other approaches in precipitation prediction.

Anahtar Kelimeler

Multiscale Geographically Weighted Regression, MGWR, Data Mining, Precipitation

Etik Beyan

We declare that our study do not require ethical committee permission

Destekleyen Kurum

Erciyes University

Proje Numarası

FBA-2022-12224

Teşekkür

This study is supported by the Erciyes University Research Fund (FBA-2022-12224).

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