1. bookVolume 49 (2022): Edizione 1 (January 2022)
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eISSN
1338-7014
Prima pubblicazione
16 Apr 2017
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access type Accesso libero

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

Pubblicato online: 30 Dec 2021
Volume & Edizione: Volume 49 (2022) - Edizione 1 (January 2022)
Pagine: 23 - 34
Ricevuto: 24 Apr 2021
Accettato: 12 Oct 2021
Dettagli della rivista
License
Formato
Rivista
eISSN
1338-7014
Prima pubblicazione
16 Apr 2017
Frequenza di pubblicazione
2 volte all'anno
Lingue
Inglese
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.

Keywords

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