Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2024, Cilt: 11 Sayı: 2, 61 - 66, 16.06.2024
https://doi.org/10.30897/ijegeo.1399172

Öz

Proje Numarası

FBA-2022-12224

Kaynakça

  • Ashiq, M. W., Zhao, C., Ni, J., Akhtar, M. (2010). GIS-based high-resolution spatial interpolation of precipitation in mountain–plain areas of Upper Pakistan for regional climate change impact studies. Theoretical and Applied Climatology, 99(3), 239-253.
  • Brunsdon, C., McClatchey, J., Unwin, D. J. (2001). Spatial variations in the average rainfall–altitude relationship in Great Britain: an approach using geographically weighted regression. International Journal of Climatology, 21(4), 455-466.
  • Celik, M., Dadaser-Celik, F., Dokuz, A. S. (2014). Discovery of hydrometeorological patterns. Turkish Journal of Electrical Engineering and Computer Sciences, 22(4), 3.
  • da Silva, A. R., de Oliveira Lima, A. (2017). Geographically Weighted Beta Regression. Spatial Statistics, 21, 279-303.
  • Diodato, N. (2005). The influence of topographic co-variables on the spatial variability of precipitation over small regions of complex terrain. International Journal of Climatology, 25(3), 351-363.
  • Dong, G., Nakaya, T., Brunsdon, C. (2018). Geographically weighted regression models for ordinal categorical response variables: An application to geo-referenced life satisfaction data. Computers, Environment and Urban Systems, 70, 35-42.
  • Fotheringham, A., Crespo, R., Yao, J. (2015). Geographical and Temporal Weighted Regression (GTWR). Geographical Analysis, 47.
  • Fotheringham, A. S., Brunsdon, C., M. Charlton. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley.
  • Fotheringham, A. S., Yang, W., Kang, W. (2017). Multiscale Geographically Weighted Regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265.
  • Harris, R., Singleton, A., Grose, D., Brunsdon, C., Longley, P. (2010). Grid-enabling Geographically Weighted Regression: A Case Study of Participation in Higher Education in England. Transactions in GIS, 14(1), 43-61.
  • Hsueh, Y.-H., Lee, J., Beltz, L. (2012). Spatio-temporal patterns of dengue fever cases in Kaoshiung City, Taiwan, 2003–2008. Applied Geography, 34, 587-594.
  • Hung Tien, T., Hiep Tuan, N., Viet-Trung, T. (2016, 6-8 Oct. 2016). Large-scale geographically weighted regression on Spark. Paper presented at the 2016 Eighth International Conference on Knowledge and Systems Engineering (KSE).
  • Leong, Y.-Y., Yue, J. C. (2017). A modification to geographically weighted regression. International Journal of Health Geographics, 16(1), 11.
  • Li, Z., Fotheringham, A., Li, W., Oshan, T. (2018). Fast Geographically Weighted Regression (FastGWR): A Scalable Algorithm to Investigate Spatial Process Heterogeneity in Millions of Observations. International Journal of Geographical Information Science.
  • Lu, B., Brunsdon, C., Charlton, M., Harris, P. (2017). Geographically weighted regression with parameter-specific distance metrics. International Journal of Geographical Information Science, 31(5), 982-998.
  • Ma, X., Zhang, J., Ding, C., Wang, Y. (2018). A geographically and temporally weighted regression model to explore the spatiotemporal influence of built environment on transit ridership. Computers, Environment and Urban Systems, 70, 113-124.
  • Mays, L. (2001). Water Resources Engineering. New York: John Wiley & Sons, New York.
  • Tasyurek, M., Celik, M. (2020). RNN-GWR: A geographically weighted regression approach for frequently updated data. Neurocomputing, 399, 258-270.
  • Tasyurek, M., Celik, M. (2022). 4D-GWR: geographically, altitudinal, and temporally weighted regression. Neural Computing and Applications, 34(17), 14777-14791.
  • Taşyürek, M., Celik, M. (2021). FastGTWR: A fast geographically and temporally weighted regression approach. Journal of the Faculty of Engineering and Architecture of Gazi University, 36, 715-726.
  • Wang, K., Zhang, C., Li, W. (2013). Predictive mapping of soil total nitrogen at a regional scale: A comparison between geographically weighted regression and cokriging. Applied Geography, 42, 73-85.
  • Wei, C.-H., Qi, F. (2012). On the estimation and testing of mixed geographically weighted regression models. Economic Modelling, 29(6), 2615-2620.
  • Zhang, H., Zhang, J., Lu, S., Cheng, S., Zhang, J. (2011). Modeling hotel room price with geographically weighted regression. International Journal of Hospitality Management, 30(4), 1036-1043.

Prediction of Precipitation using Multiscale Geographically Weighted Regression

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

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).

Kaynakça

  • Ashiq, M. W., Zhao, C., Ni, J., Akhtar, M. (2010). GIS-based high-resolution spatial interpolation of precipitation in mountain–plain areas of Upper Pakistan for regional climate change impact studies. Theoretical and Applied Climatology, 99(3), 239-253.
  • Brunsdon, C., McClatchey, J., Unwin, D. J. (2001). Spatial variations in the average rainfall–altitude relationship in Great Britain: an approach using geographically weighted regression. International Journal of Climatology, 21(4), 455-466.
  • Celik, M., Dadaser-Celik, F., Dokuz, A. S. (2014). Discovery of hydrometeorological patterns. Turkish Journal of Electrical Engineering and Computer Sciences, 22(4), 3.
  • da Silva, A. R., de Oliveira Lima, A. (2017). Geographically Weighted Beta Regression. Spatial Statistics, 21, 279-303.
  • Diodato, N. (2005). The influence of topographic co-variables on the spatial variability of precipitation over small regions of complex terrain. International Journal of Climatology, 25(3), 351-363.
  • Dong, G., Nakaya, T., Brunsdon, C. (2018). Geographically weighted regression models for ordinal categorical response variables: An application to geo-referenced life satisfaction data. Computers, Environment and Urban Systems, 70, 35-42.
  • Fotheringham, A., Crespo, R., Yao, J. (2015). Geographical and Temporal Weighted Regression (GTWR). Geographical Analysis, 47.
  • Fotheringham, A. S., Brunsdon, C., M. Charlton. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley.
  • Fotheringham, A. S., Yang, W., Kang, W. (2017). Multiscale Geographically Weighted Regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265.
  • Harris, R., Singleton, A., Grose, D., Brunsdon, C., Longley, P. (2010). Grid-enabling Geographically Weighted Regression: A Case Study of Participation in Higher Education in England. Transactions in GIS, 14(1), 43-61.
  • Hsueh, Y.-H., Lee, J., Beltz, L. (2012). Spatio-temporal patterns of dengue fever cases in Kaoshiung City, Taiwan, 2003–2008. Applied Geography, 34, 587-594.
  • Hung Tien, T., Hiep Tuan, N., Viet-Trung, T. (2016, 6-8 Oct. 2016). Large-scale geographically weighted regression on Spark. Paper presented at the 2016 Eighth International Conference on Knowledge and Systems Engineering (KSE).
  • Leong, Y.-Y., Yue, J. C. (2017). A modification to geographically weighted regression. International Journal of Health Geographics, 16(1), 11.
  • Li, Z., Fotheringham, A., Li, W., Oshan, T. (2018). Fast Geographically Weighted Regression (FastGWR): A Scalable Algorithm to Investigate Spatial Process Heterogeneity in Millions of Observations. International Journal of Geographical Information Science.
  • Lu, B., Brunsdon, C., Charlton, M., Harris, P. (2017). Geographically weighted regression with parameter-specific distance metrics. International Journal of Geographical Information Science, 31(5), 982-998.
  • Ma, X., Zhang, J., Ding, C., Wang, Y. (2018). A geographically and temporally weighted regression model to explore the spatiotemporal influence of built environment on transit ridership. Computers, Environment and Urban Systems, 70, 113-124.
  • Mays, L. (2001). Water Resources Engineering. New York: John Wiley & Sons, New York.
  • Tasyurek, M., Celik, M. (2020). RNN-GWR: A geographically weighted regression approach for frequently updated data. Neurocomputing, 399, 258-270.
  • Tasyurek, M., Celik, M. (2022). 4D-GWR: geographically, altitudinal, and temporally weighted regression. Neural Computing and Applications, 34(17), 14777-14791.
  • Taşyürek, M., Celik, M. (2021). FastGTWR: A fast geographically and temporally weighted regression approach. Journal of the Faculty of Engineering and Architecture of Gazi University, 36, 715-726.
  • Wang, K., Zhang, C., Li, W. (2013). Predictive mapping of soil total nitrogen at a regional scale: A comparison between geographically weighted regression and cokriging. Applied Geography, 42, 73-85.
  • Wei, C.-H., Qi, F. (2012). On the estimation and testing of mixed geographically weighted regression models. Economic Modelling, 29(6), 2615-2620.
  • Zhang, H., Zhang, J., Lu, S., Cheng, S., Zhang, J. (2011). Modeling hotel room price with geographically weighted regression. International Journal of Hospitality Management, 30(4), 1036-1043.
Toplam 23 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Fiziksel Coğrafya ve Çevre Jeolojisi (Diğer)
Bölüm Research Articles
Yazarlar

Murat Taşyürek 0000-0001-5623-8577

Mete Çelik 0000-0002-1488-1502

Ali Ümran Kömüşcü 0000-0001-9930-2479

Filiz Dadaser-celik 0000-0003-3623-7723

Proje Numarası FBA-2022-12224
Yayımlanma Tarihi 16 Haziran 2024
Gönderilme Tarihi 1 Aralık 2023
Kabul Tarihi 11 Haziran 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 11 Sayı: 2

Kaynak Göster

APA Taşyürek, M., Çelik, M., Kömüşcü, A. Ü., Dadaser-celik, F. (2024). Prediction of Precipitation using Multiscale Geographically Weighted Regression. International Journal of Environment and Geoinformatics, 11(2), 61-66. https://doi.org/10.30897/ijegeo.1399172