Comparison of artificial intelligence techniques for prediction of soil temperatures in Turkey

Çıtakoğlu, Hatice

doi:10.1007/s00704-016-1914-7

Cited by 60 publications

(35 citation statements)

References 44 publications

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“…It is imperative to note that trial and error procedures implemented for finding the optimal structures of the models. This followed the fact that there is not any definite verified process for indicating the ideal number of hidden neurons and parameters, and these are typically selected in an iterative manner (Deo & Sahin, 2015, 2017. Additionally, after examining the different training algorithms and transfer functions, the Levenberg-Marquardt algorithm and the sigmoid transfer function were selected for the current research.…”

Section: Resultsmentioning

confidence: 99%

“…Soil temperature (ST) and its spatial and temporal changes directly or indirectly affect the extent and direction of many processes occurring in soil (Mehdizadeh et al, 2018) such as seed germination, root elongation, evaporation, storage and movement of water and microbial activities, nutrient cycle, and many other dynamic processes of the soil (Beltrami, 2001;Citakoglu, 2017;Qian et al, 2011). Soil temperature is affected by several factors, including topography, solar radiation, air temperature, precipitation, soil moisture content and soil thermal conductivity, and heat transfer coefficients (Bilgili, 2010(Bilgili, , 2011Hillel, 1998).…”

Section: Introductionmentioning

confidence: 99%

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Comparative analysis of hybrid models of firefly optimization algorithm with support vector machines and multilayer perceptron for predicting soil temperature at different depths

Shamshirband

Esmaeilbeiki

Zarehaghi

et al. 2020

Engineering Applications of Computational Fluid Mechanics

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative analysis of hybrid models of firefly optimization algorithm with support vector machines and multilayer perceptron for predicting soil temperature at different depths

Shamshirband

Esmaeilbeiki

Zarehaghi

et al. 2020

Engineering Applications of Computational Fluid Mechanics

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“…crucial variable for balancing the interaction of heat energy between the atmosphere and the land surface [2]. In addition, it is relevant to many research fields, such as soil science, agricultural applications and hydrologic engineering [3]- [5].…”

Section: Introductionmentioning

confidence: 99%

GANs-LSTM Model for Soil Temperature Estimation From Meteorological: A New Approach

Hao

Zhao

et al. 2020

IEEE Access

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Soil temperature (T s) is a vital meteorological parameter for ecological, physical and biological research. T s estimation is very important for a variety of fields and presents great challenges because the relevant areas have complex characteristics, human activities, and a nonlinear nature between T s and its environmental factors. Hence, a novel model based on long short-term memory (LSTM), is proposed here as an alternative data-intelligence tool. The proposed model designs a novel function that combines LSTM loss with an adversarial term for enhancing the correlation between T s and its environmental factors. For this purpose, we designed a novel function that combines LSTM loss with an adversarial term. The adversarial term encourages the LSTM model to estimate T s , which cannot be distinguished from the observed T s by an adversarial model. In this study, the proposed model is trained using meteorological information from two stations in China (Changbai Mountain and Haibei). The model is constructed using hourly input variables, including air temperature (T a), wind speed (W), relative humidity (RH), solar radiation (SR), and vapor pressure deficit(VPD), while the objective variable is the T s measurement at a 5 cm depth for the period 2003-2005. Different statistical evaluation criteria, including the root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe model efficiency coefficient (NS), Willmott index of agreement(WI) and the Legates and McCabe index (LMI), have been employed to assess the model performance. Through experimentation, the proposed model generally performs superior to the other seven state-of-the-art estimation models (autoregressive integrated moving average model, linear regression, backpropagation neural networks, support vector regression, extreme learning machine, eXtreme gradient boosting and long short-term memory) in T s estimation at a 5 cm depth over the Changbai Mountain and Haibei stations. For this case, the most accurate performance is attained for T s estimation at a 5 cm depth, with the highest values of NS = 0.915, WI = 0.978, and LMI = 0.686, and the lowest values of the relative RMSE and MAE being 2.276 and 1.796, respectively. In accordance with the present results, it is concluded that the proposed model can serve as an alternative approach for estimating T s , while ensuring that an appropriate combination of meteorological inputs is applied to yield an optimal model. INDEX TERMS Data-driven model, soil temperature, LSTM, adversarial training.

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“…Over the last years, the AI-based techniques have been proposed in various areas of research, such as meteorological, hydrological and soil sciences (Tabari et al, 2011;Talaee, 2014;Aitkenhead and Coull, 2016;Liu et al, 2016;Citakoglu, 2017;Mehdizadeh et al, 2017aMehdizadeh et al, , 2018aGavili et al, 2018;Massawe et al, 2018;Singh et al, 2018;Maroufpoor et al, 2019;Azad et al, 2020;Mehdizadeh, 2020). For example, the AI approaches were applied successfully by Mehdizadeh (2018a), Mehdizadeh et al (2017b) and Azad et al (2020) to estimate the dew point and air temperatures.…”

Section: Introductionmentioning

confidence: 99%

“…For example, the ST can be estimated by artificial intelligence (AI)‐based models, such as adaptive neuro‐fuzzy inference systems (ANFIS), artificial neural networks (ANN), multivariate adaptive regression splines (MARS), M5 model tree (M5T), gene expression programming (GEP) and support vector machine (SVM) (Nahvi et al ., 2016; Citakoglu, 2017; Sanikhani et al ., 2018). Besides the AI‐based models, time‐series analysis (TSA) approaches could also be appropriate tools for the ST estimation (Mehdizadeh et al ., 2020a).…”

Section: Introductionmentioning

confidence: 99%

Modelling daily soil temperature at different depths via the classical and hybrid models

Mehdizadeh

Ahmadi

Sales³

2020

Meteorological Applications

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Soil temperature (ST) is one of the crucial variables of soil and it plays a fundamental role in different research scopes such as underground soil physical and agricultural applications. The study explores the modelling performance of a time series‐based model (i.e. bi‐linear, BL), and an artificial intelligence‐based approach including adaptive neuro‐fuzzy inference system (ANFIS), for modelling the daily ST of different soil depths (5, 10, 50 and 100 cm). The study also develops and proposes two diverse types of the hybrid models through coupling the ANFIS with the BL and wavelet analysis (W) to improve the accuracy of the ST modelling. Two stations in Iran (i.e. Isfahan and Urmia) were selected as the study locations. The results demonstrated that the ANFIS generally presented better results than the BL. Furthermore, the hybrid models (i.e. W‐ANFIS and ANFIS‐BL) gave superior performances than the classical ANFIS and BL for modelling the daily ST of the studied areas at various soil depths. In addition to the local evaluation of the ANFIS (i.e. modelling the ST at a specific depth by using the original ST data at that depth), an external analysis was also conducted. In doing so, the daily ST data at a 5 cm depth were modelled via the corresponding ST data at a 10 cm depth, and vice versa. The results denoted the applicability of the ST data at another depth for modelling the ST of each specific/target depth.

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Comparison of artificial intelligence techniques for prediction of soil temperatures in Turkey

Cited by 60 publications

References 44 publications

Comparative analysis of hybrid models of firefly optimization algorithm with support vector machines and multilayer perceptron for predicting soil temperature at different depths

Comparative analysis of hybrid models of firefly optimization algorithm with support vector machines and multilayer perceptron for predicting soil temperature at different depths

GANs-LSTM Model for Soil Temperature Estimation From Meteorological: A New Approach

Modelling daily soil temperature at different depths via the classical and hybrid models

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