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An Artificial Neural Network to Simulate Surface Runoff and Soil Erosion in Burned Forests

Capitolo di libro
Data di Pubblicazione:
2022
Citazione:
An Artificial Neural Network to Simulate Surface Runoff and Soil Erosion in Burned Forests / Fotia, L.; Lucas-Borja, M. E.; Rosaci, D.; Sarne, G. M. L.; Zema, D. A.. - 1026:(2022), pp. 113-122. [10.1007/978-3-030-96627-0_11]
Abstract:
Few experiences of Artificial Neural Networks (ANNs) for hydrological predictions in forest soils after wildfire and post-fire treatments are available in literature. To fill this gap, an ANN model has been adapted to predict surface runoff and soil erosion in Mediterranean burned pine forests (Central Spain), and tested against hydrological observations at plot scale throughout 2 years. The model gave very accurate runoff and erosion predictions in burned and non-burned soils as well as for all soil treatments (mulching and/or logging or not). Although further experimental tests are needed to validate the ANN applicability to soils in burned and treated forests in other ecosystems, the use of ANN may be useful for landscape planners as decision support system for the integrated assessment and management of forests.
Tipologia CRIS:
2.1 Contributo in volume (Capitolo o Saggio)
Keywords:
-
Elenco autori:
Fotia, L.; Lucas-Borja, M. E.; Rosaci, D.; Sarne, G. M. L.; Zema, D. A.
Autori di Ateneo:
ROSACI Domenico
ZEMA Demetrio Antonio
Link alla scheda completa:
https://iris.unirc.it/handle/20.500.12318/130027
Titolo del libro:
Studies in Computational Intelligence
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URL

https://link.springer.com/chapter/10.1007/978-3-030-96627-0_11#citeas
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