Daily soil temperature modeling using neuro-fuzzy approach

Talaee, P. Hosseinzadeh

doi:10.1007/s00704-013-1084-9

Cited by 44 publications

(14 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The input combinations of the ANN models tried in this work are presented in Table . Because the present study aimed to develop simple ANN models for soil temperature forecasting, in addition to antecedent soil temperatures, only air temperature as the most effective variable for soil temperature (Tabari et al , ; Hosseinzadeh Talaee, ) was added to the input vector of the ANN models. As shown in Table , six input combinations were evaluated for the ANN models, of which the first three combinations are based on antecedent soil temperatures and the rest are based on antecedent soil temperatures plus concurrent and antecedent air temperatures.…”

Section: Application and Resultsmentioning

confidence: 99%

“…In recent years, several studies have reported that the ANN with its ability to model non‐linear relationships may offer a promising alternative for soil temperature modelling. Although several applications of ANNs for this type of modelling exist (George, ; Mihalakakou, ; Bilgili, ; Ozturk et al , ; Tabari et al , ; Bilgili et al , ; Wu et al , ; Hosseinzadeh Talaee, ; Kim and Singh, ; Kisi et al , ), they have so far been restricted to the research environment. The outcomes of such researches are encouraging, as the ANN method has been found to be very useful in providing important information regarding the non‐linear characteristics of soil temperature and its predictability.…”

Section: Introductionmentioning

confidence: 99%

“…The outcomes of such researches are encouraging, as the ANN method has been found to be very useful in providing important information regarding the non‐linear characteristics of soil temperature and its predictability. Some of these studies used meteorological data as inputs for the ANN models (e.g., George, ; Mihalakakou, ; Bilgili, ; Tabari et al , ; Hosseinzadeh Talaee, ). Another study by Bilgili et al () estimated soil temperatures of a target station using only the soil temperatures of neighbouring stations without any consideration of the other variables or parameters related to soil properties.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Short-term forecasting of soil temperature using artificial neural network

2014

Self Cite

View full text Add to dashboard Cite

Soil temperature is one of the most important meteorological parameters that plays a critical role in land surface hydrological processes. In the current study, artificial neural network (ANN) models were developed and tested for 1 day ahead soil temperature forecasting at 5, 10, 20, 30, 50 and 100 cm depths. Antecedent soil temperatures plus concurrent and antecedent air temperatures were used as inputs for the ANN models. Soil and air temperature data were collected from two Iranian weather stations located in humid and arid regions for the period 2004-2005. The models' accuracies were evaluated using the Nash-Sutcliffe co-efficient of efficiency, the correlation co-efficient, the root mean square error and the mean bias error between the observed and forecasted soil temperature values. The Nash-Sutcliffe co-efficient of efficiency values >0.94 and correlation co-efficient >0.96 for all the ANN models show that the models can be applied successfully to provide accurate and reliable short-term soil temperature forecasts.

show abstract

Section: Application and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Short-term forecasting of soil temperature using artificial neural network

2014

Self Cite

View full text Add to dashboard Cite

show abstract

“…During the last two decades, the machine learning methods have been applied and showed high effectiveness and accurate performance to several engineering applications, especially for forecasting, prediction, pattern recognition problems. In 2014, Coactive Neuro-Fuzzy Inference System (CANFIS) has been employed to forecast the daily soil temperature in arid and semi-arid areas by [12]. Relatively good performance for forecasting the soil temperature has been achieved, however, the range of the maximum error was slightly high.…”

Section: Introductionmentioning

confidence: 99%

“…It has been reported that the major challenges for achieving accurate predicting of the soil temperature is unavailability of the most meteorological variables needed as the model inputs [12]. In addition, the prediction of the soil temperature has inner uncertainties in terms of the measurement sensors' precision, a noise because of sensors and the nonlinear feature interrelationship.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

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

Mehdizadeh

Ahmadi

Sales³

2020

Meteorological Applications

View full text Add to dashboard Cite

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.

show abstract

Daily soil temperature modeling using neuro-fuzzy approach

Cited by 44 publications

References 20 publications

Short-term forecasting of soil temperature using artificial neural network

Short-term forecasting of soil temperature using artificial neural network

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

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

Contact Info

Product

Resources

About