Prediction of Soil Fractions (Sand, Silt and Clay) in Surface Layer Based on Natural Radionuclides Concentration in the Soil Using Adaptive Neuro Fuzzy Inference System

Al-Hamed, Saad A.; Wahby, M. F.; Al-Sulaiman, Mohamed; Aboukarima, Abdulwahed M.

doi:10.4236/ojss.2014.47024

Cited by 13 publications

(3 citation statements)

References 19 publications

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“…These were attributed to the moderate nutrient retention and releasing rate in the newly consolidated land. Soil structural and hydrological properties, as well as nutrient availability, depend on soil texture related to soil particle distribution [31]. Guest red and loess materials significantly increased soil clay and silt contents (Figure 4a,b).…”

Section: Discussionmentioning

confidence: 99%

Land Engineering Consolidates Degraded Sandy Land for Agricultural Development in the Largest Sandy Land of China

Wang

2020

Land

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Sandification has become a major obstacle to China’s regional farmland protection, economic development, and ecological civilization construction. It is urgent to adopt advanced ideas and practical actions to reverse the sandy land. Structural consolidation theory was introduced to rehabilitate sandy land into farmland by soil body building, soil layer reconstruction, and soil quality improvement. A field experiment was conducted in Mu Us Sandy Land to explore the effects of blended guest materials (red clay and loess) with sand at four volume ratios (1:1, 1:2, 1:3 and 1:5) on crop yields, soil properties, and root growth. Red clay and loess significantly increased clay and silt contents and regulated the soil total nitrogen concentration and organic matter content during the critical growth stage of maize. Red clay and loess had a significant promotion of maize and soybean yields at a volume ratio of 1:3. The maximum potato yield was 42,501 and 37,332 kg ha−1 in red clay treatment at a volume ratio of 1:5 and in loess treatment at a volume ratio of 1:3, respectively. Lowest root biomass in surface soil and surface/subsoil root biomass ratio mediated maize growth in red clay treatment. Red clay was considered as the better material to rehabilitate sandy land and develop agriculture in the long-term according to the engineering costs and crop yields. Water sustainable utilization strategies and potential popularization areas of sandy land structural consolidation should be enhanced in the future.

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Section: Discussionmentioning

confidence: 99%

Land Engineering Consolidates Degraded Sandy Land for Agricultural Development in the Largest Sandy Land of China

Wang

2020

Land

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“…Many agricultural researchers used ANFIS in the prediction of soil contamination. Furthermore, it was used to predict particle size distribution [28]. The ANFIS was trained and tested using data from this study's field experiments.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Predicting Heavy Metal Contamination Based on Adaptive Neuro-Fuzzy Inference System and GIS in an Arid Ecosystem

et al. 2023

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The issue of agricultural soil pollution is especially important as it directly affects the quality of food and the lives of humans and animals. Soil pollution is linked to human activities and agricultural practices. The main objective of this study is to assess and predict soil contamination by heavy metals utilizing an innovative method based on the adaptive neuro-fuzzy inference system (ANFIS), an effective artificial intelligence technology, and GIS in a semiarid and dry environment. A total of 150 soil samples were randomly collected in the neighboring area of the Bahr El-Baqar drain. Ordinary kriging (OK) was employed to generate spatial pattern maps for the following heavy metals: chromium (Cr), iron (Fe), cadmium (Cd), and nickel (Ni). The adaptive neuro-fuzzy inference system (ANFIS), known as one of the most effective applications of artificial intelligence (AI), was utilized to predict soil contamination by the selected heavy metals (Cr, Fe, Cd, and Ni). In total 150 samples were used, 136 soil samples were used for training and 14 for testing. The ANFIS predicting results were compared with the experimental results; this comparison proved its effectiveness, as a root mean square error (RMSE) was 0.048594 in training, and 0.0687 in testing, which is an acceptable result. The results showed that both the exponential and spherical models were quite suitable for Cr, Fe, and Ni. The correlation values (R2) were close to one in training and test; however, the stable model performed well with Cd. The high concentration of heavy metals was the most prevalent, encompassing approximately 51.6% of the study area. Furthermore, the average concentration of heavy metals in this degree was 82.86 ± 15.59 mg kg−1 for Cr, 20,963.84 ± 4447.83 mg kg−1 for Fe, 1.46 ± 0.42 mg kg−1 for Cd, and 48.71 ± 11.88 mg kg−1 for Ni. The comparison clearly demonstrates that utilizing the ANFIS model is a superior option for predicting the level of soil pollution. Ultimately, these findings can serve as a foundation for decision-makers to develop acceptable measures for mitigating heavy metal contamination.

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“…Additionally, an adaptive neuro-fuzzy inference system (ANFIS) has been used by Murlidhar et al [25] to study the shear strength of rock based on the internal friction angle. Das and Basudhar [34] and Al-Hamed et al [35] have developed ANN models to predict the internal friction angle of soil. Besides, Pham et al [36] have developed a hybrid model using random forest and particle swarm optimization for estimation of undrained shear strength of soil.…”

Section: Introductionmentioning

confidence: 99%

Evolution of Deep Neural Network Architecture Using Particle Swarm Optimization to Improve the Performance in Determining the Friction Angle of Soil

Pham

Tran

2021

Mathematical Problems in Engineering

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This study focuses on the use of deep neural network (DNN) to predict the soil friction angle, one of the crucial parameters in geotechnical design. Besides, particle swarm optimization (PSO) algorithm was used to improve the performance of DNN by selecting the best structural DNN parameters, namely, the optimal numbers of hidden layers and neurons in each hidden layer. For this aim, a database containing 245 laboratory tests collected from a project in Ho Chi Minh city, Vietnam, was used for the development of the proposed hybrid PSO-DNN model, including seven input factors (soil state, standard penetration test value, unit weight of soil, void ratio, thickness of soil layer, top elevation of soil layer, and bottom elevation of soil layer) and the friction angle was considered as the target. The data set was divided into three parts, namely, the training, validation, and testing sets for the construction, validation, and testing phases of the model. Various quality assessment criteria, namely, the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE), were used to estimate the performance of PSO-DNN models. The PSO algorithm showed a remarkable ability to find out an optimal DNN architecture for the prediction process. The results showed that the PSO-DNN model using 10 hidden layers outperformed the DNN model, in which the average correlation improvement increased R2 by 1.83%, MAE by 5.94%, and RMSE by 8.58%. Besides, a global sensitivity analysis technique was used to detect the most important inputs, and it showed that, among the seven input variables, the elevation of top and bottom of soil played an important role in predicting the friction angle of soil.

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Prediction of Soil Fractions (Sand, Silt and Clay) in Surface Layer Based on Natural Radionuclides Concentration in the Soil Using Adaptive Neuro Fuzzy Inference System

Cited by 13 publications

References 19 publications

Land Engineering Consolidates Degraded Sandy Land for Agricultural Development in the Largest Sandy Land of China

Land Engineering Consolidates Degraded Sandy Land for Agricultural Development in the Largest Sandy Land of China

A Novel Approach for Predicting Heavy Metal Contamination Based on Adaptive Neuro-Fuzzy Inference System and GIS in an Arid Ecosystem

Evolution of Deep Neural Network Architecture Using Particle Swarm Optimization to Improve the Performance in Determining the Friction Angle of Soil

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