首页|University of Florence (UNIFI)Reports Findings in Machine Learning (Optimizatio n of SVR and CatBoost models using metaheuristic algorithms to assess landslide susceptibility)
University of Florence (UNIFI)Reports Findings in Machine Learning (Optimizatio n of SVR and CatBoost models using metaheuristic algorithms to assess landslide susceptibility)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Machine Learning is the subject o f a report.According to news reporting out of Florence, Italy, by NewsRx editor s, research stated, “In this study, a landslide susceptibility assessment is per formed by combining two machine learning regression algorithms (MLRA), such as s upport vector regression (SVR) and categorical boosting (CatBoost), with two pop ulation-based optimization algorithms, such as grey wolf optimizer (GWO) and par ticle swarm optimization (PSO), to evaluate the potential of a relatively new al gorithm and the impact that optimization algorithms can have on the performance of regression models.The Kerala state in India has been chosen as the test site due to the large number of recorded incidents in the recent past.”
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