Soil temperature modeling at different depths using neuro-fuzzy, neural network, and genetic programming techniques

Kisi, Özgür; Hadi, Syamsul; Çobaner, Murat

doi:10.1007/s00704-016-1810-1

Cited by 68 publications

(30 citation statements)

References 21 publications

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“…In the study, monthly climatic data, air temperature (T), relative humidity (RH), solar radiation (SR), wind speed (W) and soil temperature for the depths of 5, 10, 50, and 100 cm were obtained from Mersin station (longitude 34˚38 0 E, latitude 36˚48 0 N, altitude 3 m) which is operated by Turkish Meteorological Service. The study area (Fig 1) has Mediterranean climate with wet winters and dry summers [21,11]. The winter can get very heavy rains and flooding is a big problem in some regions.…”

Section: Used Datamentioning

confidence: 99%

“…It should be noted that in few cases most of these variables might not available and the interrelationship between the soil temperature and these variables are highly non-linear. Such truth, the ability of the machine learning models motivates the researchers to be utilized as the most effective technique to accurately predict the soil temperature [9,10,11].…”

Section: Introductionmentioning

confidence: 99%

“…It has been verified that the soil temperature forecasting accuracy has been slightly improved using the SaE-ELM model. Furthermore, in 2017, Adaptive Neuro-Fuzzy Inference System (ANFIS), Gene Expression Programming (GEP), and Artificial Neural Network (ANN) methods have been utilized as a modeling technique for estimating the soil temperature (ST) at various depths for two different stations in Turkey by [11]. It has been reported that the GEP method outperformed the other methods attaining better accuracy for forecasting the soil temperature at all depths.…”

Section: Introductionmentioning

confidence: 99%

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Advanced machine learning model for better prediction accuracy of soil temperature at different depths

et al. 2020

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Soil temperature has a vital importance in biological, physical and chemical processes of terrestrial ecosystem and its modeling at different depths is very important for land-atmosphere interactions. The study compares four machine learning techniques, extreme learning machine (ELM), artificial neural networks (ANN), classification and regression trees (CART) and group method of data handling (GMDH) in estimating monthly soil temperatures at four different depths. Various combinations of climatic variables are utilized as input to the developed models. The models' outcomes are also compared with multi-linear regression based on Nash-Sutcliffe efficiency, root mean square error, and coefficient of determination statistics. ELM is found to be generally performs better than the other four alternatives in estimating soil temperatures. A decrease in performance of the models is observed by an increase in soil depth. It is found that soil temperatures at three depths (5, 10 and 50 cm) could be mapped utilizing only air temperature data as input while solar radiation and wind speed information are also required for estimating soil temperature at the depth of 100 cm.

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Section: Used Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Advanced machine learning model for better prediction accuracy of soil temperature at different depths

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“…Overall, the performance of the ANN models increased with increasing the depth of soil. Kisi et al (2016) modeled soil temperature at different depths in Mersin and Adana, Turkey, by using ANN, ANFIS and genetic programming (GP) methods. It was found that the performance of the GP was better than ANN and ANFIS models.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the performance of artificial intelligence methods for estimation of monthly mean soil temperature without using meteorological data

2017

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Soil temperature has an important role in agricultural, hydrological, meteorological and climatological studies. In the present research, monthly mean soil temperature at four different depths (5, 10, 50 and 100 cm) was estimated using artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP). The monthly mean soil temperature data of 31 stations over Iran were employed. In this process, the data of 21 and 10 stations were used for training and testing stages of used models, respectively. Furthermore, the geographical information including latitude, longitude and altitude as well as periodicity component (the number of months) was considered as inputs in the mentioned intelligent models. The results demonstrated that the ANN and ANFIS models had good performance in comparison with the GEP model. Nevertheless, the ANFIS generally performed better than ANN model.

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“…The location of the Antalya, Isparta and Burdur cities are shown inFigure 2. The data set is taken for the 17 year (2000-2016) monthly values of air temperature, relative humidity and solar radiation and soil temperature at different depths(5,10, 20, 50, and 100 cm). The map of location of the stations in Turkey…”

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confidence: 99%

Monthly Soil Temperature Modeling Using Gene Expression Programming

Şahin

Dikmen

Kumaş

2019

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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Soil temperature is a critical variable controlling below-ground processes for global and continental carbon budgets. However, there are an insufficient number of climatic stations monitoring soil temperature. In this study, GEP model was used for estimation of monthly soil temperature using air temperature, depth, relative humidity and solar radiation data for the Antalya, Isparta, and Burdur in Turkey. This model was tested using measured meteorological data. The values of R 2 between observed and predicted soil temperatures ranged from 0.95 to 0.97. Predictions with GEP model show good agreement with actual soil temperature measurements. New equations are presented for calculation of soil temperatures at different depths. The GEP-based formulations are very practical to predict soil temperature. Soil temperature prediction with GEP model is helpful in various processes, including agricultural decision, heating or cooling of buildings and ground-source heat pump applications.

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Soil temperature modeling at different depths using neuro-fuzzy, neural network, and genetic programming techniques

Cited by 68 publications

References 21 publications

Advanced machine learning model for better prediction accuracy of soil temperature at different depths

Advanced machine learning model for better prediction accuracy of soil temperature at different depths

Evaluating the performance of artificial intelligence methods for estimation of monthly mean soil temperature without using meteorological data

Monthly Soil Temperature Modeling Using Gene Expression Programming

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