干旱区生态环境知识资源中心(Arid): Predicting the hydrological response of a forest after wildfire and soil treatments using an Artificial Neural Network

Arid

DOI	10.1016/j.compag.2020.105280
	Predicting the hydrological response of a forest after wildfire and soil treatments using an Artificial Neural Network
	Zema, Demetrio Antonio 1; Lucas-Borja, Manuel Esteban 2; Fotia, Lidia 3; Rosaci, Domenico 4; Sarne, Giuseppe M. L.3; Zimbone, Santo Marcello 1
通讯作者	Zema, Demetrio Antonio
来源期刊	COMPUTERS AND ELECTRONICS IN AGRICULTURE
ISSN	0168-1699
EISSN	1872-7107
出版年	2020
卷号	170
英文摘要	Accurate predictions of surface runoff and soil erosion after wildfire help land managers adopt the most suitable actions to mitigate post-fire land degradation and rehabilitation planning. The use of the Artificial Neural Networks (ANNs) is advisable as hydrological prediction tool, given their lower requirement of input information compared to the traditional hydrological models. This study proposes an ANN model, purposely prepared for forest areas of the semi-arid Mediterranean environments. The ANN hydrological prediction capability in non-burned, burned by wildfire, and burned and then treated soils has been verified at the plot scale in pine forests of South-Eastern Spain. Runoff and soil loss were much higher than non-burned soils (assumed as control), but mulch application was effective to control runoff and soil erosion in burned plots. Moreover, logging did not affect the hydrological response of these soils. 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), with only one exception (that is, in the condition with the combination of treatments which gave the worst performance, burning, mulching and logging), as shown by the exceptionally high model efficiency and coefficients of determination. Although further experimental tests are needed to validate the ANN applicability to the burned forests of the semi-arid conditions and other ecosystems, the use of ANN can be suggested to landscape planners as decision support system for the integrated assessment and management of forests.
英文关键词	Artificial intelligence Hydrological modelling Surface runoff Erosion Mulching Logging
类型	Article
语种	英语
国家	Italy ; Spain
收录类别	SCI-E
WOS记录号	WOS:000519652000026
WOS关键词	REDUCING POSTFIRE RUNOFF ; EROSION ; MODEL ; PINE ; SCALE ; FIRE ; INFILTRATION ; RUSLE ; HILLSLOPE ; COVER
WOS类目	Agriculture, Multidisciplinary ; Computer Science, Interdisciplinary Applications
WOS研究方向	Agriculture ; Computer Science
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/314281
作者单位	1.Univ Mediterranea Reggio Calabria, Dept AGR, I-89122 Reggio Di Calabria, Italy; 2.Univ Castilla La Mancha, Dept Ciencia & Tecnol Agroforestal & Genet, Campus Univ S-N, Albacete 02071, Spain; 3.Univ Mediterranea Reggio Calabria, Dept DICEAM, I-89122 Reggio Di Calabria, Italy; 4.Univ Mediterranea Reggio Calabria, Dept DIIES, I-89122 Reggio Di Calabria, Italy
推荐引用方式 GB/T 7714	Zema, Demetrio Antonio,Lucas-Borja, Manuel Esteban,Fotia, Lidia,et al. Predicting the hydrological response of a forest after wildfire and soil treatments using an Artificial Neural Network[J],2020,170.
APA	Zema, Demetrio Antonio,Lucas-Borja, Manuel Esteban,Fotia, Lidia,Rosaci, Domenico,Sarne, Giuseppe M. L.,&Zimbone, Santo Marcello.(2020).Predicting the hydrological response of a forest after wildfire and soil treatments using an Artificial Neural Network.COMPUTERS AND ELECTRONICS IN AGRICULTURE,170.
MLA	Zema, Demetrio Antonio,et al."Predicting the hydrological response of a forest after wildfire and soil treatments using an Artificial Neural Network".COMPUTERS AND ELECTRONICS IN AGRICULTURE 170(2020).