首页|New Findings from University of Alabama in the Area of Machine Learning Describe d (Urban Flood Susceptibility Mapping Using Frequency Ratio and Multiple Decisio n Tree-based Machine Learning Models)
New Findings from University of Alabama in the Area of Machine Learning Describe d (Urban Flood Susceptibility Mapping Using Frequency Ratio and Multiple Decisio n Tree-based Machine Learning Models)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Machine Learning are pre sented in a new report. According to news reporting from Tuscaloosa, Alabama, by NewsRx journalists, research stated, “Machine learning (ML) models, particularl y decision tree (DT)-based algorithms, are being increasingly utilized for flood susceptibility mapping. To evaluate the advantages of DT-based ML models over t raditional statistical models on flood susceptibility assessment, a comparative study is needed to systematically compare the performances of DT- based ML model s with that of traditional statistical models.”
TuscaloosaAlabamaUnited StatesNort h and Central AmericaCyborgsEmerging TechnologiesMachine LearningUnivers ity of Alabama
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