Department of Civil Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran.
Department of Civil Engineering, Ilia State University, Tbilisi, Georgia.
PLoS One. 2020 Apr 14;15(4):e0231055. doi: 10.1371/journal.pone.0231055. eCollection 2020.
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.
土壤温度在陆地生态系统的生物、物理和化学过程中具有重要意义，其在不同深度的建模对于陆地-大气相互作用非常重要。本研究比较了四种机器学习技术，即极限学习机（ELM）、人工神经网络（ANN）、分类回归树（CART）和数据处理分组方法（GMDH），用于估计四个不同深度的月土壤温度。将各种气候变量组合作为输入用于开发模型。还根据纳什-苏特克里夫效率、均方根误差和决定系数统计数据，将模型的结果与基于多元线性回归的结果进行了比较。ELM 通常在估计土壤温度方面比其他四种替代方案表现更好。随着土壤深度的增加，模型的性能会下降。结果发现，仅利用空气温度数据作为输入就可以绘制三个深度（5、10 和 50 厘米）的土壤温度图，而要估计 100 厘米深度的土壤温度则还需要太阳辐射和风速信息。
