Soil Temperature Estimation with Meteorological Parameters by Using Tree-Based Hybrid Data Mining Models

Abstract

The temperature of the soil at different depths is one of the most important factors used in different disciplines, such as hydrology, soil science, civil engineering, construction, geotechnology, ecology, meteorology, agriculture, and environmental studies. In addition to physical and spatial variables, meteorological elements are also effective in changing soil temperatures at different depths. The use of machine-learning models is increasing day by day in many complex and nonlinear branches of science. These data-driven models seek solutions to complex and nonlinear problems using data observed in the past. In this research, decision tree (DT), gradient boosted trees (GBT), and hybrid DT–GBT models were used to estimate soil temperature. The soil temperatures at 5, 10, and 20 cm depths were estimated using the daily minimum, maximum, and mean temperature; sunshine intensity and duration, and precipitation data measured between 1993 and 2018 at Divrigi station in Sivas province in Turkey. To predict the soil temperature at different depths, the time windowing technique was used on the input data. According to the results, hybrid DT–GBT, GBT, and DT methods estimated the soil temperature at 5 cm depth the most successfully, respectively. However, the best estimate was obtained with the DT model at soil depths of 10 and 20 cm. According to the results of the research, the accuracy rate of the models has also increased with increasing soil depth. In the prediction of soil temperature, sunshine duration and air temperature were determined as the most important factors and precipitation was the most insignificant meteorological variable. According to the evaluation criteria, such as Nash-Sutcliffe coefficient, R, MAE, RMSE, and Taylor diagrams used, it is recommended that all three (DT, GBT, and hybrid DT–GBT) data-based models can be used for predicting soil temperature.

Prediction of land surface temperature of major coastal cities of India using bidirectional LSTM neural networks

“…In The R 2 scores ranged between 0.73 and 0.92, NSE scores ranged between 0.66 and 0.91, RSR scores ranged between 0.30-0.58, PBIAS scores were within the limit (,+10), and MSE scores were reasonably low (∼ 2°C) during the validation periods, revealing high model reliability. The MAE scores for all the coastal cities ranged between 0.16-1.03°C pertaining to all models (Table 6), which are reasonable in comparison to earlier models of the Adaptive Neuro-Fuzzy Inference System (ANFIS) for predicting the ST approach by Mustafa et al (2020) (0.16°C); Support Vector Regression (SVR) approach for Global Solar Radiation (GSR) by Samadianfard et al (2019) (0.99°C); hybrid Decision Tree (DT), Gradient Boosted Trees (GBT) (DT-GBT) approach for predicting soil temperature by Sattari et al (2020) (0.52-0.97°C); Multilayer Perceptron (MLP) algorithm and SVR approach for predicting soil temperature by Shamshirband et al (2020) (0.72-5.17°C). The R 2 scores for all the coastal cities ranged between 0.73-0.92°C pertaining to all models (Table 6), which are reasonable in The LSTM-BiLSTM hybrid model was implemented using LSTM and BiLSTM with nine layers.…”

“…It only requires input to represent a data set containing many samples to train the algorithm. Recent studies showed the problems solved by the ML in various fields, such as hydrological and climatological applications (Samadianfard et al 2019;Sankaranarayanan et al 2019;Sattari et al 2020;Shamshirband et al 2020;Madhuri et al 2021;Sadeghfam et al 2021). ML techniques accurately and efficiently represented the unresolved problems in climate science results (Brenowitz & Bretherton 2018;O'Gorman & Dwyer 2018;Rasp et al 2018;Bolton & Zanna 2019;Salehipour & Peltier 2019).…”

Applications of Discrete Wavelet Transform for Feature Extraction to Increase the Accuracy of Monitoring Systems of Liquid Petroleum Products

“…In recent years, various advanced computational approaches have been applied in different fields such as chemical engineering [37][38][39][40][41][42][43][44], control and electrical engineering [45][46][47][48][49][50][51][52][53][54][55][56][57][58][59], pharmacy and medical science [60][61][62][63][64][65][66][67], industrial engineering [68,69], civil engineering [70][71][72][73], economic and business sciences, [74][75][76][77][78], mechanical engineering [79][80][81][82][83][84][85], energy engineering…”

A Novel Machine Learning Approach: Soil Temperature Ordinal Classification (STOC)

“…Machine learning has recently received much attention in the soil temperature prediction field (Sattari et al 2020;Li et al 2020;Abyaneh et al 2016). It discovers significant underlying patterns in raw data by constructing a model without any human intervention.…”