Charles Hulme, Joshua McGrane, Mihaela Duta, Gillian West, Denise Cripps, Abhishek Dasgupta, Sarah Hearne, Rachel Gardner, Margaret Snowling
LanguageScreen: The Development, Validation, and Standardization of an Automated Language Assessment App
Language, speech, and hearing services in schools
streamflow time series data typically exhibit nonlinear nonstationary characteristics complicate precise estimation recently multifactorial machine learning ml models developed enhance performance streamflow predictions however lack interpretability within ml models raises concerns inner workings reliability paper introduces innovative hybrid architecture tcnlstmmultiheadattention model combines two layers temporal convolutional networks tcn followed one layer long shortterm memory lstm units integrated multiheadattention mechanism predicting streamflow streamflow causationdriven prediction samples rcdp employing local global interpretability studies shapley values partial dependency analysis findpeaks method used identify peak flow events test dataset validating models generality uncovering physical causative patterns streamflow results show 1 compared lstm model hyperparameter settings proposed tcnlstmmultiheadattention hybrid model increased r2 529 25 431 107 respectively four stations test set predictions using rcdp samples moreover comparing prediction results hybrid model different samples hengshan station r2 rcdp increased 506 122 compared streamflow autoregressive prediction samples rap meteorologicalsoil volumetric water content coupled autoregressive prediction samples mcsap respectively 2 historical streamflow data preceding 3 days predominantly influences predictions due strong autocorrelation flow quantity q typically emerging significant feature alongside precipitation p surface soil moisture ssm adjacent station flow data 3 periods low normal flow historical data remains crucial factor however flood periods roles upstream inflow precipitation become significantly pronounced model facilitates identification quantification various hydrodynamic impacts flow predictions including upstream flood propagation precipitation soil moisture conditions also elucidates models nonlinear relationships threshold responses thereby enhancing interpretability reliability streamflow predictions