A Comprehensive Study of Artificial Intelligence Applications for Soil Temperature Prediction in Ordinary Climate Conditions and Extremely Hot Events

Abstract

Soil temperature is a fundamental parameter in water resources and irrigation engineering. A cost-effective model that can accurately forecast soil temperature is urgently needed. Recently, many studies have applied artificial intelligence (AI) at both surface and underground levels for soil temperature predictions. In the present study, attempts are made to deliver a comprehensive and detailed assessment of the performance of a wide range of AI approaches in soil temperature prediction. In this regard, thirteen approaches, from classic regressions to well-established methods of random forest and gradient boosting to more advanced AI techniques, such as multi-layer perceptron and deep learning, are taken into account. Meanwhile, great varieties of land and atmospheric variables are applied as model inputs. A sensitivity analysis was conducted on input climate variables to determine the importance of each variable in predicting soil temperature. This examination reduced the number of input variables from 8 to 7, which decreased the simulation load. Additionally, this showed that air temperature and solar radiation play the most important roles in soil temperature prediction, while precipitation can be neglected in forecast AI models. The comparison of soil temperature predicted by different AI models showed that deep learning demonstrated the best performance with R-squared of 0.980 and NRMSE of 2.237%, followed by multi-layer perceptron with R-squared of 0.980 and NRMSE of 2.266%. In addition, the performance of developed AI models was evaluated in extremely hot events since heat warnings are essential to protect lives and properties. The assessment showed that deep learning and multi-layer perceptron methods still have the best prediction. However, their R-squared decreased to 0.862 and 0.859, and NRMSE increased to 6.519% and 6.601%, respectively.

A Convolutional Neural Network Model for Soil Temperature Prediction under Ordinary and Hot Weather Conditions: Comparison with a Multilayer Perceptron Model

“…Figure 10 shows the distribution of the predicted soil temperatures between the prediction bands under ordinary weather conditions. Prediction bands and the confidence region are widely used in the statistical analysis of regression prediction [27]. This figure proves the strength of the 1D CNN and MLP models considering that the distribution of the soil temperature predicted remains between the prediction bands in both their training and testing phases.…”

“…They concluded that ANFIS with the sunflower optimizer performed better than the other models. Imanian et al (2022) assessed the performance of 13 machine learning models, including different classic regression models, ANFIS, SVM, KNN, RF, GB, and MLP, for forecasting hourly soil temperatures under ordinary and extreme weather conditions [27]. The input data of these models encompassed eight hourly climatic features.…”

Artificial intelligence to predict soil temperatures by development of novel model

“…Mainly that’s because of the possibility of them understanding the data pattern variations. LSTMs and Gradient Boost algorithms were well utilized for these scenarios 46 . Applying the state-of-the-art model undergoes several steps when it comes to real-world applications.…”

Development of a robust daily soil temperature estimation in semi-arid continental climate using meteorological predictors based on computational intelligent paradigms

“…Imanian et al (2022) [ 48 ] thoroughly evaluate the effectiveness of various AI methods in predicting ST parameter. They considered different approaches, including both traditional regression techniques and more advanced methods such as deep learning.…”