Kathryn A Fuller, Kathryn A Morbitzer, Jacqueline M Zeeman, Adam M Persky, Amanda C Savage, Jacqueline E McLaughlin
Exploring the use of ChatGPT to analyze student course evaluation comments
BMC medical education
accurate identification urban waterlogging areas assessing waterlogging susceptibility crucial preventing controlling hazards datadriven models utilized forecast waterlogging areas establishing intricate relationships explanatory variables waterlogging states approach tackles constraints mechanistic models frequently complex unable incorporate socioeconomic factors previous research predominantly employed singletype datadriven models predict waterlogging locations evaluation effectiveness scarcity comprehensive performance comparisons uncertainty analyses different types models well lack interpretability analysis chosen study area central area beijing prone waterlogging given high manpower time economic costs associated collecting waterlogging information waterlogging point distribution map released beijing water affairs bureau selected labeled samples twelve factors affecting waterlogging susceptibility chosen explanatory variables construct random forest rf support vector machine radial basis function svmrbf particle swarm optimizationweakly labeled support vector machine psowellsvm maximum entropy maxent utilization diverse single evaluation indicators fscore kappa auc etc assess model performance may yield conflicting results distance indices simulation observation diso chosen comprehensive measure assess models performance predicting waterlogging points psowellsvm exhibited highest performance disotest value 063 outperforming maxent 078 excelled identifying areas highly susceptible waterlogging including extremely high susceptibility zones svmrbf rf models demonstrated suboptimal performance exhibited overfitting examination waterlogging susceptibility distribution maps predicted four models revealed significant spatial differences due variations computational principles input parameter complexities integration four wsams based logistic regression shown significantly decrease uncertainty single datadriven model identify floodprone areas improve interpretability data model geographical detector incorporated demonstrate explanatory capacity 12 variables process waterlogging building density bd exhibits highest explanatory power relation explain waterlogging susceptibility q value 0202 followed distance road frequency heavy rainstorms fhr dem etc interaction bd fhr results nonlinear increase explanatory power waterlogging susceptibility presence waterlogging susceptibility risk research area attributed interactions multiple factors