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dc.contributor.authorDjeddou, Messaoud
dc.contributor.authorHameed, Ibrahim A.
dc.contributor.authorMokhtari, Elhadj
dc.date.accessioned2020-06-30T11:08:31Z
dc.date.available2020-06-30T11:08:31Z
dc.date.created2020-01-13T11:32:14Z
dc.date.issued2019
dc.identifier.issn1098-7584
dc.identifier.urihttps://hdl.handle.net/11250/2660057
dc.description.abstractAccurate prediction of soil erosion rate is a quite important issue for a wise and sustainable use of soil resources. In this study, an adaptive neuro-fuzzy inference system (ANFIS) approach is used to construct a prediction model. The objectives of this study is to develop fuzzy logic models that predict soil erosion in a relatively large watershed using a limited number of input variables, compare the predictions of soil erosion using ANFIS model with those of the Revised Universal Soil Loss Equation RUSLE. With the incorporation of Geographical Information System (GIS), it is possible to analyse satellite data, which gives required information like land use and cover, slope, distribution of rainfall, flow direction etc. of study watershed. The capabilities of these technologies increase when they are integrated with ANFIS model for erosion prediction. ANFIS model and GIS integrated erosion prediction models do not only estimate soil loss but also provide the spatial distributions of the erosion. Generating accurate erosion risk maps in GIS environment is very important to locate the areas with high erosion risks for prioritization and to develop adequate conservation techniques for a better sustainable management of Wadi Sahel watershed (Algeria).en_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.titleSoil Erosion Rate Prediction using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Geographic Information System (GIS) of Wadi Sahel-Soummam Watershed (Algeria)en_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.volume2019-Juneen_US
dc.source.journalIEEE International Fuzzy Systems conference proceedingsen_US
dc.identifier.doi10.1109/FUZZ-IEEE.2019.8858857
dc.identifier.cristin1771265
dc.description.localcodeThis article will not be available due to copyright restrictions (c) 2019 by IEEEen_US
cristin.unitcode194,63,55,0
cristin.unitnameInstitutt for IKT og realfag
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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