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

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.

journal_name

PeerJ

journal_title

PeerJ

authors

Sadeghi-Niaraki A,Kisi O,Choi SM

doi

10.7717/peerj.8882

subject

Has Abstract

pub_date

2020-08-14 00:00:00

pages

e8882

issn

2167-8359

pii

8882

journal_volume

8

pub_type

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