PREDICTING CLOUDBURST EVENTS USING MACHINE LEARNING ALGORITHMS: A DATA-DRIVEN APPROACH TO ENHANCE WEATHER FORECASTING ACCURACY

Abstract

A system and method for predicting cloudburst events using machine learning algorithms trained on historical and real-time environmental data are provided. The system comprises a data collection module that aggregates diverse meteorological and geospatial data from sources such as ground sensors, satellites, and historical datasets. A preprocessing module cleans and normalizes the data, reducing noise and enhancing data quality. The processed data is analyzed by a machine learning module, which uses predictive models—such as neural networks, decision trees, or ensemble methods—to generate a cloudburst likelihood score. When this score surpasses a predetermined threshold, an alert generation module issues notifications to relevant users through multiple channels, including SMS, email, and mobile applications. The system can dynamically update itself through reinforcement learning, continually refining its predictive accuracy. The invention is designed to operate at various scales, from local to regional, and p

Specification

Description:The embodiments of the present invention generally relate to the field of meteorological data processing, specifically to a predictive system for forecasting short-duration, high-intensity rainfall events known as cloudbursts. This system leverages advanced machine learning algorithms and extensive environmental datasets to improve the accuracy of weather predictions, offering a scalable, data-driven approach that enhances real-time forecasting capabilities for effective disaster preparedness.
BACKGROUND OF THE INVENTION
The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

Cloudburst events are characterized by sudden, intense rainfall , Claims:1. A system for predicting cloudburst events, comprising:
a data collection module configured to aggregate weather and environmental data from multiple sources, including real-time sensors, historical meteorological datasets, and geospatial data;
a pre-processing module configured to clean, normalize, and organize the collected data temporally and spatially to enhance data quality and reduce noise;
a machine learning module comprising a predictive model trained on historical data of cloudburst events, configured to analyze incoming data patterns and generate a cloudburst likelihood score, wherein the predictive model includes at least one of a neural network, a decision tree, or an ensemble model;
an alert generation module configured to issue alerts based on the cloudburst likelihood score surpassing a predefined threshold, with notifications sent to users through multiple communication channels including SMS, email, and web-based dashboards.

2. The system of claim 1, wherein the data collection module furt

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