Implementing advanced techniques for urban mountain torrent surveillance and early warning using rainfall predictive analysis

Abstract

Urban flood forecasting and early warning play a pivotal role in ensuring efficient flood mitigation and management. The unpredictability in precipitation's intensity, temporal patterns, and spatial distribution introduces considerable variability into the basin's flow dynamics. This, in turn, escalates the uncertainty surrounding hydrological predictions, complicating the task of flood forecasting and early warning. To address these challenges, this research introduces a method that refines rainfall forecasts using a Wavelet Neural Network (WNN). By establishing a benchmark for area rainfall, we've developed a comprehensive disaster prevention and early warning system that synergizes real-time precipitation data, area rainfall, and flood peak predictions. Specifically tailored for urban terrains prone to mountain torrents, the WNN-based monitoring and pre-alarm model offers a sound and practical forecasting tool. Its relevance is accentuated by its potential to spearhead urban flood control initiatives. Our findings validate the model's adaptability and efficacy, particularly within urban mountainous watersheds, heralding a fresh paradigm in mountain flood disaster forecasting and early warnings.