Mayank Agarwal, Ayan Goswami, Priyanka Sharma
Evaluating ChatGPT-3.5 and Claude-2 in Answering and Explaining Conceptual Medical Physiology Multiple-Choice Questions
Cureus
prediction suspended sediment load ssl within riverine systems critical understanding watersheds hydrology therefore novelty research developing interpretable explainable model based deep learning dl shapley additive explanations shap interpretation technique prediction ssl riverine systems paper investigates abilities four dl models including dense deep neural networks ddnn long shortterm memory lstm gated recurrent unit gru simple recurrent neural network rnn models prediction daily ssl using river discharge rainfall data daily time scale taleghan river watershed northwestern tehran iran performance models evaluated using several quantitative graphical criteria effect parameter settings performance deep models ssl prediction also investigated optimal optimization algorithms maximum iteration mi batch size bc obtained modeling daily ssl structure model impact prediction remarkably comparison prediction accuracy models illustrated ddnn r2 096 rmse 33346 outperformed lstm r2 075 rmse 78620 gru r2 073 rmse 82567 simple rnn r2 078 rmse 74145 furthermore taylor diagram confirmed ddnn highest performance among models interpretation techniques address blackbox nature models shap applied develop interpretable dl model interpret dl models output results shap showed river discharge strongest impact models output estimating ssl overall conclude dl models great potential watersheds predict ssl therefore different interpretation techniques tools interpret dl models output dl model blackbox model recommended future research