Development support vector machine, artificial neural network and artificial neural network – genetic algorithm hybrid models for estimating erodible fraction of soil to wind erosion

Nouri, Alireza; Esfandiari, Mehrdad; Eftekhari, Kamran; ‎Torkashvand, Ali Mohammadi; Ahmadi, Abbas

doi:10.1080/15715124.2022.2153856

Cited by 4 publications

(2 citation statements)

References 52 publications

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“…The process is repeated for four times, because we have four target variables with the same model architecture. During the training process, the model learns to optimize the weights of its connections by minimizing the error between the predicted and actual values (Nouri et al, 2023). The model's performance is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared (R 2 ), and Mean Absolute Error (MAE).…”

Section: Application Of Artificial Neural Network To Find Out Best Seimentioning

confidence: 99%

“…The hidden layers perform intermediate computations between the input and output layers, and each neuron's output is determined by an activation function. The activation function introduces non-linearity into the model and enables it to learn complex relationships between the input features and the target variables (Nouri et al, 2023).…”

Section: Application Of Artificial Neural Network To Find Out Best Seimentioning

confidence: 99%

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An artificial intelligence-based assessment of soil erosion probability indices and contributing factors in the Abha-Khamis watershed, Saudi Arabia

Alqadhi,

Mallick,

Talukdar

et al. 2023

Front. Ecol. Evol.

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Soil erosion is a major problem in arid regions, including the Abha-Khamis watershed in Saudi Arabia. This research aimed to identify the soil erosional probability using various soil erodibility indices, including clay ratio (CR), modified clay ratio (MCR), Critical Level of Soil Organic Matter (CLOM), and principle component analysis based soil erodibility index (SEI). To achieve these objectives, the study used t-tests and an artificial neural network (ANN) model to identify the best SEI model for soil erosion management. The performance of the models were then evaluated using R2, Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE), with CLOM identified as the best model for predicting soil erodibility. Additionally, the study used Shapley additive explanations (SHAP) values to identify influential parameters for soil erosion, including sand, clay, silt, soil organic carbon (SOC), moisture, and void ratio. This information can help to develop management strategies oriented to these parameters, which will help prevent soil erosion. The research showed notable distinctions between CR and CLOM, where the 25–27% contribution explained over 89% of the overall diversity. The MCR indicated that 70% of the study area had low erodibility, while 20% had moderate and 10% had high erodibility. CLOM showed a range from low to high erodibility, with 40% of soil showing low CLOM, 40% moderate, and 20% high. Based on the T-test results, CR is significantly different from CLOM, MCR, and principal component analysis (PCA), while CLOM is significantly different from MCR and PCA, and MCR is significantly different from PCA. The ANN implementation demonstrated that the CLOM model had the highest accuracy (R2 of 0.95 for training and 0.92 for testing) for predicting soil erodibility, with SOC, sand, moisture, and void ratio being the most important variables. The SHAP analysis confirmed the importance of these variables for each of the four ANN models. This research provides valuable information for soil erosion management in arid regions. The identification of soil erosional probability and influential parameters will help to develop effective management strategies to prevent soil erosion and promote agricultural production. This research can be used by policymakers and stakeholders to make informed decisions to manage and prevent soil erosion.

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Section: Application Of Artificial Neural Network To Find Out Best Seimentioning

confidence: 99%

Section: Application Of Artificial Neural Network To Find Out Best Seimentioning

confidence: 99%

An artificial intelligence-based assessment of soil erosion probability indices and contributing factors in the Abha-Khamis watershed, Saudi Arabia

Alqadhi,

Mallick,

Talukdar

et al. 2023

Front. Ecol. Evol.

View full text Add to dashboard Cite

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Artificial neural network-based optimization of baffle geometries for maximized heat transfer efficiency in microchannel heat sinks

Shuqi,

Limei,

Goyal

et al. 2023

Case Studies in Thermal Engineering

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A Data-Driven Approach for Assessing the Wind-Induced Erodible Fractions of Soil

Motameni,

Soroush,

Fattahi

et al. 2023

Preprint

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Development support vector machine, artificial neural network and artificial neural network – genetic algorithm hybrid models for estimating erodible fraction of soil to wind erosion

Cited by 4 publications

References 52 publications

An artificial intelligence-based assessment of soil erosion probability indices and contributing factors in the Abha-Khamis watershed, Saudi Arabia

An artificial intelligence-based assessment of soil erosion probability indices and contributing factors in the Abha-Khamis watershed, Saudi Arabia

Artificial neural network-based optimization of baffle geometries for maximized heat transfer efficiency in microchannel heat sinks

A Data-Driven Approach for Assessing the Wind-Induced Erodible Fractions of Soil

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