Optimization Of Rocky Desertification Control Model Based On Case-based Reasoning And Intelligent Algorithm

Karst rocky desertification is currently the most prominent environmentalissues and research focus in field of karst. Karst rocky desertification refers to thesubtropicalkarstregionsenvironmentalbackground,drivenbyunreasonablehumanactivities,causing serious soil erosion,large areas of exposed bedrock,landproductivitysharplyreduceandsurfacesimilardesertlandscapeoflanddegradationprocess.CurrentlyagroupofsouthwestChinakarstrockydesertificationareaprojectisinthepilotimplementation,andmadeanumberofgovernance,buttherockydesertification progress is still slow,there are still many problems. How use newideasandnewmethodisaccurate,objectiveandeffectivecontrolandmaintenanceof karst rocky desertification environmental health safety. All,is one of theinterdisciplinarymajorissue.Similarity‐based reasoning more and more attention,there CBR (Case‐BasedReasoning,CBR)conceptandthebasicideaofcase‐basedreasoningisconsistentwithhumanreasoningpatterns.Rule‐basedsystemsinmanyareascandonothing,butthecase‐basedsystem,applicationsalmostwithoutlimit,case‐basedreasoninginknowledgerepresentationisbasedoncase‐based,caseforeasieraccessthantherule,greatlysimplifyingtheknowledgeacquisition forthepast.Theresultswerere‐used,canimprovetheefficiencyofsolvingnewproblems. IntheCBRreasoning,accordingtonewcasesandexistingcasesinthecasebasetodeterminethesimilarityofrockydesertificationtypesofmembership,inthereasoningprocesscanreducethe use of membership are many uncertainties,making the classification resultsmorereasonable,comparingdifferentmethodsofclassificationresultsshowthattheestablishedcaselibrarycanbereused.Thesouthwestenregionofthecurrentdesertificationcontrolmoresuccessfulmodelofgovernanceinapreliminaryclassification,andtheinitialestablishmentofindex system of karst rocky desertification control cases. Achieved using theGA‐BP‐ANNmodeloftheinitialcaseofdesertificationcontrolreasoning,andmadeamoresignificantresults. BP neural network is based on the gradient algorithm derived,evolutionaryconvergencerateisslow,easytofallintolocaloptimum.SincetheinitialweightsofBP‐neural network is a random value and the threshold,resulting in network isvulnerabletofallingintolocaloptimalvalue,andaftereachtrainingsessiontherearedifferences between the network predicted output. The BP neural network andgeneticalgorithmcombination,theinitialnetworkusinggeneticalgorithmtofindtheoptimalweightsandthresholdvalues,canmakeupfortheinitialweightsandthresholdvaluesofrandomdefects, sothenetworkcanmoreaccuratelypredictthesystemoutput,andtheresultsofreasoningmoreaccurateprediction.Meanwhile,this paper established the GA‐BP neural network base case‐based reasoning indesertification control mode in the empirical analysis,and achieved a moresatisfactoryresults.TheproposedreasoningmodelwillforecastthefuturegovernanceofSouthwestChina Karst , a new way of thinking and method for the development ofdesertificationgovernancemodelcanprovidereference.