Spatial modeling of long-term air temperatures for sustainability: evolutionary fuzzy approach and neuro-fuzzy methods

Abolghasem Sadeghi-Niaraki; Ozgur Kisi; Soo-Mi Choi

doi:10.7717/peerj.8882

Spatial modeling of long-term air temperatures for sustainability: evolutionary fuzzy approach and neuro-fuzzy methods

PeerJ. 2020 Aug 14:8:e8882. doi: 10.7717/peerj.8882. eCollection 2020.

Authors

Abolghasem Sadeghi-Niaraki^{1

2}, Ozgur Kisi³, Soo-Mi Choi²

Affiliations

¹ Geoinformation Tech. Center of Excellence, Faculty of Geomatics Engineering, K.N. Toosi University of Technology, Tehran, Iran.
² Department of Computer Science and Engineering, Sejong University, Seoul, South Korea.
³ Faculty of Natural Sciences and Engineering, Ilia State University, Tbilisi, Georgia.

Abstract

This paper investigates the capabilities of the evolutionary fuzzy genetic (FG) approach and compares it with three neuro-fuzzy methods-neuro-fuzzy with grid partitioning (ANFIS-GP), neuro-fuzzy with subtractive clustering (ANFIS-SC), and neuro-fuzzy with fuzzy c-means clustering (ANFIS-FCM)-in terms of modeling long-term air temperatures for sustainability based on geographical information. In this regard, to estimate long-term air temperatures for a 40-year (1970-2011) period, the models were developed using data for the month of the year, latitude, longitude, and altitude obtained from 71 stations in Turkey. The models were evaluated with respect to mean absolute error (MAE), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and the determination coefficient (R2). All data were divided into three parts and every model was tested on each. The FG approach outperformed the other models, enhancing the MAE, RMSE, NSE, and R2 of the ANFIS-GP model, which yielded the highest accuracy among the neuro-fuzzy models by 20%, 30%, and 4%, respectively. A geographical information system was used to obtain temperature maps using estimates of the optimal models, and the results of the model were assessed using it.

Keywords: Estimation; Evolutionary fuzzy; GIS; Long-term air temperature; Neuro-fuzzy.

Grants and funding

This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2020-2016-0-00312). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.