<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Splash erosion, as the first step of soil erosion, causes the movement of the soil particles and lumps and is considered an important process in soil erosion. Given the complexity of this process in nature, one way of identifying and modeling the process is to use a rainfall simulator and to study it under laboratory circumstances. For this purpose, transported material was measured with various rainfall intensities and different amounts of poly-acryl-amide. In the next step, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM) were used to model the transported materials. The results showed that among the three methods, the best values of evaluation criteria were related to SVM, and ANFIS respectively. Among the three studied durations, the experiment with a duration of 30 minutes received the best results. The results based on available data showed by increasing the number of membership functions, over-fitting happens in the ANFIS method. To reduce the complexity of the model and the likelihood of over-fitting, some rules were eliminated. The results showed that the performance of the model improved by eliminating some rules.</p>
</abstract>

Splash erosion, as the first step of soil erosion, causes the movement of the soil particles and lumps and is considered an important process in soil erosion. Given the complexity of this process in nature, one way of identifying and modeling the process is to use a rainfall simulator and to study it under laboratory circumstances. For this purpose, transported material was measured with various rainfall intensities and different amounts of poly-acryl-amide. In the next step, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM) were used to model the transported materials. The results showed that among the three methods, the best values of evaluation criteria were related to SVM, and ANFIS respectively. Among the three studied durations, the experiment with a duration of 30 minutes received the best results. The results based on available data showed by increasing the number of membership functions, over-fitting happens in the ANFIS method. To reduce the complexity of the model and the likelihood of over-fitting, some rules were eliminated. The results showed that the performance of the model improved by eliminating some rules.

A comparative assessment between artificial neural network, neuro-fuzzy, and support vector machine models in splash erosion modelling under simulation circumstances

Department of Water Sciences and Engineering, College of Agricultural and Natural Resources

Nature Engineering Department, Faculty of Natural Resource and Environment

Folia Oecologica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Splash erosion, as the first step of soil erosion, causes the movement of the soil particles and lumps and is considered an important process in soil erosion....

A comparative assessment between artificial neural network, neuro-fuzzy, and support vector machine models in splash erosion modelling under simulation circumstances

Data publikacji: 30 gru 2021

Zakres stron: 23 - 34

Otrzymano: 24 kwi 2021

Przyjęty: 12 paź 2021

DOI: https://doi.org/10.2478/foecol-2022-0003

Słowa kluczowe
ANN, ANFIS, rainfall simulator, sediment load, soil particles, SVM

© 2022 Mahdi Boroughani et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A comparative assessment between artificial neural network, neuro-fuzzy, and support vector machine models in splash erosion modelling under simulation circumstances

Data publikacji: 30 gru 2021

Zakres stron: 23 - 34

Otrzymano: 24 kwi 2021

Przyjęty: 12 paź 2021

DOI: https://doi.org/10.2478/foecol-2022-0003

Słowa kluczoweANN, ANFIS, rainfall simulator, sediment load, soil particles, SVM

© 2022 Mahdi Boroughani et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
ANN, ANFIS, rainfall simulator, sediment load, soil particles, SVM