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基于遗传模拟退火算法的滑坡位移预测方法（英文）.docx

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基于遗传模拟退火算法的滑坡位移预测方法（英文） Title:AGeneticSimulatedAnnealing-basedApproachforLandslideDisplacementPrediction Abstract: Landslidesposeasignificantthreattoinfrastructureandhumanlives,necessitatingeffectivelandslidedisplacementpredictionmethods.ThispaperproposesanovelapproachbasedonthecombinationofGeneticAlgorithm(GA)andSimulatedAnnealing(SA)topredictlandslidedisplacement.TheGeneticSimulatedAnnealing(GSA)algorithmoptimizestheparametersusedinthepredictionmodel,resultinginimprovedaccuracyandreliabilityoflandslidedisplacementforecasts.Inthispaper,weoutlinetheframeworkoftheGSAapproachandpresentexperimentalresultsdemonstratingitseffectivenessonreal-worldlandslidedatasets. 1.Introduction: Landslidesarenaturalhazardsthatcancauseseveredamagetoproperty,infrastructure,andlossoflife.Accuratepredictionoflandslidedisplacementcanhelpmitigatetheserisksbyenablingearlywarningsystemsandinformeddecision-making.Traditionallandslidedisplacementpredictionmethodshavelimitationsintermsofaccuracyandreliability.Thispaperpresentsanovelapproach,usingGSA,toimprovetheaccuracyoflandslidedisplacementpredictions. 2.Background: 2.1LandslideDisplacementPrediction: Landslidedisplacementpredictioninvolvesestimatingthemovementofsoilmassesduringalandslideevent.Variousfactorscontributetolandslidedisplacement,includinggeologicalandgeotechnicalproperties,rainfallpatterns,andslopegeometry.Traditionalpredictionmethodstypicallyrelyonstatisticalregressiontechniquesorothersimplisticmodelsthatoftenfailtocapturethecomplexinteractionsbetweenthesefactors. 2.2GeneticAlgorithm: GeneticAlgorithmisapopulation-basedoptimizationtechniqueinspiredbytheprinciplesofnaturalevolution.Itmimicstheprocessofnaturalselection,iteration,andreproductiontofindgoodsolutionsinalargesearchspace.GeneticAlgorithmhasbeensuccessfullyappliedtosolvevariousoptimizationproblemsandhasshownpromiseinlandslideprediction. 2.3SimulatedAnnealing: SimulatedAnnealingisametaheuristicoptimizationalgorithminspiredbytheannealingprocessinmetallurgy.Itrandomlyexplorest