ANN and GRNN-Based Coupled Model for Flood Inundation Mapping of the Punpun River Basin

Abstract

The Punpun River is primarily a rain-fed river. Forecasting rainfall accurately would enable an early evaluation of drought and flooding conditions. Therefore, having a flawless model for predicting rainfall is important for the hydrological analysis of any river basin. In this study, Artificial Neural Network (ANN)-based models were developed to predict rainfall and discharge in the basin. During the rainy season, water is spread in and around the area of the watershed, thus a General Regression Neural Network (GRNN)-based model was proposed for fast estimation of the inundation area during the flood taking as input cross-section, rainfall, and discharge. The proposed ANN-GRNN coupled model is the first of its kind for this study area. The assessment of the results shows that the proposed GRNN-based model is capable of estimating the water-spreading area.

Artificial Neural Network Performance Modeling and Evaluation of Additive Manufacturing 3D Printed Parts

“…In conclusion, the ANN model presented in this study demonstrated its effectiveness in predicting the mechanical properties of 3D-printed parts. By leveraging the capabilities of ANNs, significant advancements can be made in many fields [22][23][24], and in this case, the field of additive manufacturing, leading to enhanced product performance and increased efficiency. Future research can explore further applications of ANNs in optimizing other aspects of the 3D printing process, such as material selection, geometry optimization, and process parameter tuning, to unlock the full potential of this innovative technology.…”