Spatial Interpolation of Soil Temperature and Water Content in the Land-Water Interface Using Artificial Intelligence

Imanian, Hanifeh; Shirkhani, Hamidreza; Mohammadian, Abdolmajid; Cobo, Juan Hiedra; Payeur, Pierre

doi:10.3390/w15030473

Cited by 10 publications

(4 citation statements)

References 27 publications

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“…It has the capability of identifying unseen patterns, and complex interconnections in the data series. Machine learning models are capable of predicting nonlinear relationships with high accuracy 35 , 36 . Therefore, several (8) state-of-the-art machine learning models were utilized to predict the surface and 10 cm depth soil temperature.…”

Section: Study Area and Datasetmentioning

confidence: 99%

Artificial intelligence to predict soil temperatures by development of novel model

Mampitiya,

Rozumbetov,

Rathnayake

et al. 2024

Sci Rep

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Soil temperatures at both surface and various depths are important in changing environments to understand the biological, chemical, and physical properties of soil. This is essential in reaching food sustainability. However, most of the developing regions across the globe face difficulty in establishing solid data measurements and records due to poor instrumentation and many other unavoidable reasons such as natural disasters like droughts, floods, and cyclones. Therefore, an accurate prediction model would fix these difficulties. Uzbekistan is one of the countries that is concerned about climate change due to its arid climate. Therefore, for the first time, this research presents an integrated model to predict soil temperature levels at the surface and 10 cm depth based on climatic factors in Nukus, Uzbekistan. Eight machine learning models were trained in order to understand the best-performing model based on widely used performance indicators. Long Short-Term Memory (LSTM) model performed in accurate predictions of soil temperature levels at 10 cm depth. More importantly, the models developed here can predict temperature levels at 10 cm depth with the measured climatic data and predicted surface soil temperature levels. The model can predict soil temperature at 10 cm depth without any ground soil temperature measurements. The developed model can be effectively used in planning applications in reaching sustainability in food production in arid areas like Nukus, Uzbekistan.

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Section: Study Area and Datasetmentioning

confidence: 99%

Artificial intelligence to predict soil temperatures by development of novel model

Mampitiya,

Rozumbetov,

Rathnayake

et al. 2024

Sci Rep

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show abstract

“…It can deal with relatively complex input and output relationships, and the training speed is fast. Therefore, it is widely used in the field of geotechnical engineering [60,61].…”

Section: (4) Radial Basis Function (Rbf)mentioning

confidence: 99%

Prediction of the Unconfined Compressive Strength of Salinized Frozen Soil Based on Machine Learning

Zhao,

Bing

2024

Buildings

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Unconfined compressive strength (UCS) is an important parameter of rock and soil mechanical behavior in foundation engineering design and construction. In this study, salinized frozen soil is selected as the research object, and soil GDS tests, ultrasonic tests, and scanning electron microscopy (SEM) tests are conducted. Based on the classification method of the model parameters, 2 macroscopic parameters, 38 mesoscopic parameters, and 19 microscopic parameters are selected. A machine learning model is used to predict the strength of soil considering the three-level characteristic parameters. Four accuracy evaluation indicators are used to evaluate six machine learning models. The results show that the radial basis function (RBF) has the best UCS predictive performance for both the training and testing stages. In terms of acceptable accuracy and stability loss, through the analysis of the gray correlation and rough set of the three-level parameters, the total amount and proportion of parameters are optimized so that there are 2, 16, and 16 macro, meso, and micro parameters in a sequence, respectively. In the simulation of the aforementioned six machine learning models with the optimized parameters, the RBF still performs optimally. In addition, after parameter optimization, the sensitivity proportion of the third-level parameters is more reasonable. The RBF model with optimized parameters proved to be a more effective method for predicting soil UCS. This study improves the prediction ability of the UCS by classifying and optimizing the model parameters and provides a useful reference for future research on salty soil strength parameters in seasonally frozen regions.

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“…R 2 is a statistical metric used to assess the performance of a regression machine learning model. This metric can be expressed as a function of MSE, so that as MSE increases, R 2 decreases, and its desired value is 1 [38].…”

Section: Evaluation Metricsmentioning

confidence: 99%

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

et al. 2023

Self Cite

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Soil temperature is a critical parameter in soil science, agriculture, meteorology, hydrology, and water resources engineering, and its accurate and cost-effective determination and prediction are very important. Machine learning models are widely employed for surface, near-surface, and subsurface soil temperature predictions. The present study employed a properly designed one-dimensional convolutional neural network model to predict the hourly soil temperature at a subsurface depth of 0–7 cm. The annual input dataset for this model included eight hourly climatic features. The performance of this model was assessed using a wide range of evaluation metrics and compared to that of a multilayer perceptron model. A detailed sensitivity analysis was conducted on each feature to determine its importance in predicting the soil temperature. This analysis showed that air temperature had the greatest impact and surface thermal radiation had the least impact on soil temperature prediction. It was concluded that the one-dimensional convolutional model performed better than the multilayer perceptron model in predicting the soil temperature under both normal and hot weather conditions. The findings of this study demonstrated the capability of the model to predict the daily maximum soil temperature.

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Spatial Interpolation of Soil Temperature and Water Content in the Land-Water Interface Using Artificial Intelligence

Cited by 10 publications

References 27 publications

Artificial intelligence to predict soil temperatures by development of novel model

Artificial intelligence to predict soil temperatures by development of novel model

Prediction of the Unconfined Compressive Strength of Salinized Frozen Soil Based on Machine Learning

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

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