New empirical equation to estimate the soil moisture content based on thermal properties using machine learning techniques

Sanuade, Oluseun Adetola; Hassan, Amjed; Akanji, Adesoji Olumayowa; Olaojo, Abayomi Adesola; Oladunjoye, Michael A.; Abdulraheem, Abdulazeez

doi:10.1007/s12517-020-05375-x

Cited by 35 publications

(17 citation statements)

References 45 publications

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“…Moisture content: The moisture content of soil can be defined as the ratio of the amount of water held in the soil to that in dry soil [ 110 ]. The mass of water can be computed as the difference before and after drying the soil.…”

Section: Important Input Parameters Ai Techniques and Performancmentioning

confidence: 99%

A Survey on Applications of Artificial Intelligence for Pre-Parametric Project Cost and Soil Shear-Strength Estimation in Construction and Geotechnical Engineering

Sharma

Ahmed

Naseem

et al. 2021

Sensors

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Ensuring soil strength, as well as preliminary construction cost and duration prediction, is a very crucial and preliminary aspect of any construction project. Similarly, building strong structures is very important in geotechnical engineering to ensure the bearing capability of structures against external forces. Hence, in this first-of-its-kind state-of-the-art review, the capability of various artificial intelligence (AI)-based models toward accurate prediction and estimation of preliminary construction cost, duration, and shear strength is explored. Initially, background regarding the revolutionary AI technology along with its different models suited for geotechnical and construction engineering is presented. Various existing works in the literature on the usage of AI-based models for the abovementioned applications of construction and maintenance are presented along with their advantages, limitations, and future work. Through analysis, various crucial input parameters with great impact on the estimation of preliminary construction cost, duration, and soil shear strength are enumerated and presented. Lastly, various challenges in using AI-based models for accurate predictions in these applications, as well as factors contributing to the cost-overrun issues, are presented. This study can, thus, greatly assist civil engineers in efficiently using the capabilities of AI for solving complex and risk-sensitive tasks, and it can also be used in Internet of things (IoT) environments for automated applications such as smart structural health-monitoring systems.

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Section: Important Input Parameters Ai Techniques and Performancmentioning

confidence: 99%

A Survey on Applications of Artificial Intelligence for Pre-Parametric Project Cost and Soil Shear-Strength Estimation in Construction and Geotechnical Engineering

Sharma

Ahmed

Naseem

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…It gives the average magnitude of difference between the actual and predicted values shown in Eq. (1).…”

Section: Maementioning

confidence: 99%

“…Soil moisture has a significant impact on plant diversity, crop yield, sustenance of any agricultural system, soil thermal properties, and estimating flood, slope failure, and erosion [1]. Monitoring and evaluating soil moisture can be costly because of the sensors and their regular maintenance.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Multi-output Regression Model for Soil Moisture Prediction

KÜÇÜK

Birant

Taşer

2021

Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation

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Soil moisture prediction plays a vital role in developing plants, soil properties, and sustenance of agricultural systems. Considering this motivation, in this study, an intelligent Multi-output regression method was implemented on daily values of meteorological and soil data obtained from Kemalpaşa-Örnekköy station in Izmir, Turkey, at three soil depths (15, 30, and 45 cm) between the years 2017 and 2019. In this study, nine different machine learning algorithms (Linear Regression (LR), Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator (Lasso), Random Forest (RF), Extra Tree Regression (ETR), Adaptive Boosting (AdaBoost), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and Histogram-Based Gradient Boosting (HGB)) were compared each other in terms of MAE, RMSE, and R 2 metrics. The experiments indicate that the implemented Multi-output regression models show good soil moisture prediction performance. Also, the ETR algorithm provided the best prediction performance with an 0.81 R 2 value among the other models.

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“…In recent years, with the extensive research on machine learning technology, deep learning has been widely used in many fields [7][8][9][10][11]. Currently, in regards to moisture dynamics prediction, neural network models are often applied to soil, crops, wood, etc [12][13][14][15][16][17]. There is a complex interaction between moisture changes and meteorological factors in these objects, and only by fully extracting the features of this complex relationship can reliable prediction results be obtained [18,19].…”

Section: Introductionmentioning

confidence: 99%

A Novel ABRM Model for Predicting Coal Moisture Content

Zhai

et al. 2022

J Intell Robot Syst

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Coal moisture content monitoring plays an important role in carbon reduction and clean energy decisions of coal transportation-storage aspects. Traditional coal moisture content detection mechanisms rely heavily on detection equipment, which can be expensive or difficult to deploy under field conditions. To achieve fast prediction of coal moisture content, a novel neural network model based on attention mechanism and bidirectional ResNet-LSTM structure (ABRM) is proposed in this paper. The prediction of coal moisture content is achieved by training the model to learn the relationship between changes of coal moisture content and meteorological conditions. The experimental results show that the proposed method has superior performance in terms of moisture content prediction accuracy compared with other state-of-the-art methods, and that ABRM model approaches appear to have the greatest potential for predicting coal moisture content shifts in the face of meteorological elements.

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New empirical equation to estimate the soil moisture content based on thermal properties using machine learning techniques

Cited by 35 publications

References 45 publications

A Survey on Applications of Artificial Intelligence for Pre-Parametric Project Cost and Soil Shear-Strength Estimation in Construction and Geotechnical Engineering

A Survey on Applications of Artificial Intelligence for Pre-Parametric Project Cost and Soil Shear-Strength Estimation in Construction and Geotechnical Engineering

An Intelligent Multi-output Regression Model for Soil Moisture Prediction

A Novel ABRM Model for Predicting Coal Moisture Content

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