首页|Reports Summarize Support Vector Machines Findings from Sao Paulo State Universi ty (UNESP) (Flood Susceptibility Mapping In River Basins: a Risk Analysis Using Ahp-topisis-2 N Support and Vector Machine)
Reports Summarize Support Vector Machines Findings from Sao Paulo State Universi ty (UNESP) (Flood Susceptibility Mapping In River Basins: a Risk Analysis Using Ahp-topisis-2 N Support and Vector Machine)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Machine Learning - Su pport Vector Machines have been presented. According to news reporting out of So rocaba, Brazil, by NewsRx editors, research stated, “Due to the damage caused by floods, mapping areas susceptible to this natural phenomenon plays a fundamenta l role in environmental planning. Therefore, it becomes essential to understand and map the conditions and factors involved in areas affected by geo-hydro-meteo rological events.” Financial support for this research came from Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES). Our news journalists obtained a quote from the research from Sao Paulo State Uni versity (UNESP), “In this context, we mapped areas susceptible to flooding using the AHP-TOPSIS-2 N, Support Vector Machine (SVM), and a hybrid model, AHP-SVM, the Sorocaba-Medio Tiete basin, that is a subtropical, densely populated river b asin located in Brazilian territory. We considered 11 conditioning factors relat ed to hydrogeomorphological and anthropological characteristics, and 382 histori cal flood and non-flood points. We assessed the accuracy of the modeling using t he Area Under the Curve - AUC. The AHP-SVM model presented the best efficiency a mong the models analyzed (AUC = 0.962). The principal conditioning factors relat ed to flooding were land cover and land use.”
SorocabaBrazilSouth AmericaEmergin g TechnologiesMachine LearningSupport Vector MachinesVector MachinesSao Paulo State University (UNESP)
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