METHOD FOR DETECTING FLOOD EVENTS USING MACHINE LEARNING

Abstract

ABSTRACT A method (100) for detecting flood events using machine learning. Further, the method comprising collecting data from flood-affected regions, including climatic, hydrological, and geographical data, along with labeled satellite imagery indicating flooding. pre-processing the collected data to normalize, clean, and transform it into a suitable format for analysis. Further, the method (100) comprising the steps of extracting relevant features from the pre-processed data using a feature extraction technique to enhance flood detection accuracy. Further, the method (100) comprising the steps of training a convolutional neural network (CNN) on the processed dataset to identify flood events based on the extracted features. Further, the method (100) comprising the steps of utilizing the trained CNN model for real-time flood prediction by analyzing incoming satellite images and providing alerts to facilitate timely disaster response. <>

Specification

Description:FIELD OF THE DISCLOSURE
[0001] This invention generally relates to the field of flood detection and management, and in particular relates to a method for utilizing machine learning techniques to predict flood events through the analysis of satellite imagery and environmental data, aimed at enhancing disaster preparedness and response efforts.
BACKGROUND
[0002] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
[0003] Flooding is one of the most common and devastating natural disasters, causing significant damage to infrastructure, ecosyst , Claims:1. A method (100) for detecting flood events using machine learning, the method comprising the steps of:
collecting data from flood-affected regions, including climatic, hydrological, and geographical data, along with labeled satellite imagery indicating flooding;
pre-processing the collected data to normalize, clean, and transform it into a suitable format for analysis;
extracting relevant features from the pre-processed data using a feature extraction technique to enhance flood detection accuracy;
training a convolutional neural network (CNN) on the processed dataset to identify flood events based on the extracted features; and
utilizing the trained CNN model for real-time flood prediction by analyzing incoming satellite images and providing alerts to facilitate timely disaster response.

2. The method (100) as claimed in claim 1, wherein the machine learning model is a convolutional neural network (CNN) specifically configured with dropout layers to reduce overfitting during training.

Documents