首页|Findings from Istanbul Medipol Universitesi Provide New Insights into Machine Le arning (Generating the Flood Susceptibility Map for Istanbul with GIS-Based Mach ine Learning Algorithms)
Findings from Istanbul Medipol Universitesi Provide New Insights into Machine Le arning (Generating the Flood Susceptibility Map for Istanbul with GIS-Based Mach ine Learning Algorithms)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on artificial intelligence is the su bject of a new report. According to news originating from the Istanbul Medipol U niversitesi by NewsRx editors, the research stated, "The main objective of the c urrent study is to generate a flood hazard map by using the machine learning alg orithms hybridized with the geographic information systems (GIS). In this regard , the province of Istanbul, which is the metropolitan city of Turkey, was select ed as the focal region within the scope of the study." Our news journalists obtained a quote from the research from Istanbul Medipol Un iversitesi: "The class imbalance was tackled through the commonly used random un der sampling (RUS) technique in order to create a fair comparison datum line. It is worth mentioning that this is the first time this approach has been used for flood hazard mapping studies in Turkey. Random forest (RF), stochastic gradient boosting (SGB), and XGBoost algorithms were used. The best predictive performan ce was obtained with the XGBoost algorithm, followed by SGB and RF, respectively . The RF and SGB models showed a 90.67% success rate in determinin g the inundation points, while the XGBoost model outperformed its counterparts w ith a 92.00% success rate in determining the inundation points. In this research, the importance levels of the flood triggering variables were fur ther investigated in order to enliven the comprehensibility of the obtained resu lts."
Istanbul Medipol UniversitesiAlgorithm sCyborgsEmerging TechnologiesMachine Learning
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