Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2067/47697
Titolo: Risk assessment of runoff generation using an artificial neural network and field plots in road and forest land areas
Autori: Dalir, Pejman
Naghdi, Ramin
Gholami, Vahid
Tavankar, Farzam
Latterini, Francesco
Venanzi, Rachele 
Picchio, Rodolfo 
Rivista: NATURAL HAZARDS 
Data pubblicazione: 2022
Abstract: 
Runoff generation potential (RGP) on hillslopes is an important issue in the forest roads network monitoring process. In this study, an artificial neural network (ANN) was used to predict RGP in forest road hillslopes. We trained, optimized, and tested the ANN by using field plot data from the Shirghalaye watershed located in the southern part of the Caspian Sea in Iran. Field plots were used to evaluate the effective factors in runoff generation, 45 plots were installed to measure actual runoff volume (RFP) in different environmental conditions including land cover, slope gradient, soil texture, and soil moisture. A multi-layer perceptron (MLP) network was implemented. The runoff volume was the output variable and the ground cover, slope gradient, initial moisture of soil, soil texture (clay, silt and sand percentage) were the network inputs. The results showed that ANN can predict runoff volume within the values of an appropriate level in the training (R2 = 0.95, mean squared error (MSE) = 0.009) and test stages (R2 = 0.80, MSE = 0.01). Moreover, the tested network was used to predict the runoff volume on the forest road hillslopes in the study area. Finally, an RGP map was generated based on the results of the prediction of the ANNs and the geographic information system (GIS) capabilities. The results showed the RGP in the forest road hillslopes was better predicted when using both an ANN and a GIS. This study provides new insights into the potential use of ANN in hydrological simulations.
URI: http://hdl.handle.net/2067/47697
ISSN: 1573-0840
DOI: 10.1007/s11069-022-05352-5
È visualizzato nelle collezioni:A1. Articolo in rivista

File in questo documento:
File Descrizione DimensioniFormato Existing users please
Risk_assessment_of_runoff_generation_using_an_arti.pdf1.31 MBAdobe PDF  Richiedi una copia
Visualizza tutti i metadati del documento

SCOPUSTM
Citations 20

6
Last Week
0
Last month
1
controllato il 26-nov-2024

Page view(s)

108
Last Week
0
Last month
0
controllato il 30-nov-2024

Google ScholarTM

Check

Altmetric


Tutti i documenti nella community "Unitus Open Access" sono pubblicati ad accesso aperto.
Tutti i documenti nella community Prodotti della Ricerca" sono ad accesso riservato salvo diversa indicazione per alcuni documenti specifici