JJEM: Special Edition 2 (December 2024)
JJEM: Special Edition 2 (December 2024)
2024-12-08
Flood forecasting using machine learning.
Spoorthy H P, Adarsh M J
An interesting and practical area of research is the sequences and manners of water elevation in rivers that are subject to fluctuations that may result in flooding. The aim of these flood measures is to alleviate the harmful effects of floods on both economic activity and society Predictive modeling methods variations in river levels of water, including Non- linear autoregressive model (NARX) and Support Vector Machine (SVM), Can serve as predict impending flooding. Both techniques make use of analogous hydrological and flood resource parameters, such as drizzle totals, stream inflow, peak gusts, yearly flow, the length of water damage, and other pertinent flood projection variables. The foremost important scientific investigation aspect for predicting disasters is the water temperature. Machine-learning algorithms are successful in producing forecasts because they can take data acquired from a assortment of places, classify it according to characteristics, and extrapolate it Introduction Sessions floods or over-flows. This forecasting provides an explanation of methodologies' fundamental structure through the stand- point of flood estimation.
River water levels, Flood prediction, Machine learning, Non-linear time series. n model(NARX), Support vector machine(SVM), Hydrological parameters.