Abstract
Few experiences of Artificial Neural Networks (ANNs) for hydro-logical 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 hydro-logical 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.
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Fotia, L., Lucas-Borja, M.E., Rosaci, D., Sarné, G.M.L., Zema, D.A. (2022). An Artificial Neural Network to Simulate Surface Runoff and Soil Erosion in Burned Forests. In: Camacho, D., Rosaci, D., Sarné, G.M.L., Versaci, M. (eds) Intelligent Distributed Computing XIV. IDC 2021. Studies in Computational Intelligence, vol 1026. Springer, Cham. https://doi.org/10.1007/978-3-030-96627-0_11
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DOI: https://doi.org/10.1007/978-3-030-96627-0_11
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