Temperature Modeling with the Group Method of Data Handling to Inform Projected Rainfall Depth Changes for Extreme Events in Central West, New South Wales, Australia

The focus of this research is to introduce the application polynomial neural network group method data handling (GMDH) for first time in regional area New South Wales state Australia. Within context, temperature are modeled assess its projected variation impacts on rainfall depth due climate change. study encompasses six local government areas within state’s Central West region. Stochastic methods monotonic trend identification were used support modeling. Four established homogeneity tests also assessing integrity by determining frequency breakpoints mean data. results GMDH modeling returned a coefficient determination exceeding 0.9 all stations dominated an overall upward with average maximum increase 0.459 °C per decade across found categorized as useful context applicability further change studies. By combining intensity distribution (IFD) design modification factor, increases 2070 obtained, enabling improved designs stormwater infrastructure based classified scenarios.