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

Chapter
Publication Date:
2022
Short description:
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.
Iris type:
2.1 Contributo in volume (Capitolo o Saggio)
Keywords:
-
List of contributors:
Fotia, L.; Lucas-Borja, M. E.; Rosaci, D.; Sarne, G. M. L.; Zema, D. A.
Authors of the University:
ROSACI Domenico
ZEMA Demetrio Antonio
Handle:
https://iris.unirc.it/handle/20.500.12318/130027
Book title:
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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