Koji Ooka, Runjing Liu, Munehito Arai
The Wako-Saitô-Muñoz-Eaton Model for Predicting Protein Folding and Dynamics
Molecules (Basel, Switzerland)
machine learning models show promise identifying geogenic contaminated groundwaters modeling regions limited samples challenging due need large training sets one potential solution transferring existing models regions study explores transferability high fluoride groundwater models basins shanxi rift system considering six factors including modeling methods predictor types data size samplepredictor ratio spr predictor range data informing results show transferability achieved model predictors based hydrochemical parameters rather surface parameters data informing ie adding samples challenging regions training set enhances transferability stepwise regression shows hydrochemical predictors data informing significantly improve transferability data size spr predictor range significant effects additionally despite stronger nonlinear capabilities random forests artificial neural networks necessarily surpass logistic regression transferability lastly utilize tsne algorithm generate lowdimensional representations data different basins compare representations elucidate critical role predictor types transferability