Neural-XGBoost: A Hybrid Approach for Disaster Prediction and Management Using Machine Learning

Project Code :TMMAIP472

Objective

The objective of this study is to develop a hybrid Neural-XGBoost framework that integrates CNN-based deep feature extraction with XGBoost classification for rapid, accurate, and reliable visual disaster type prediction and management.

Abstract

Natural disasters such as floods, earthquakes, and wildfires pose significant threats to human life and infrastructure worldwide. Rapid and accurate identification of disaster types from visual data is crucial for effective management and timely response. This study presents Neural-XGBoost, a hybrid machine learning framework that combines deep convolutional neural networks (CNNs) for feature extraction with an ensemble-based XGBoost classifier for robust disaster prediction. The system allows users to input disaster images, which are preprocessed through resizing and normalization before being fed into a CNN to extract high-level deep features. The extracted features from training and testing datasets, structured in labeled folders, are subsequently used to train an XGBoost equivalent model, leveraging decision tree ensembles for precise classification. Performance evaluation on unseen test images demonstrates high accuracy, precision, recall, and F1-score, highlighting the effectiveness of the hybrid approach. The framework also includes a user-friendly interface for single-image prediction, providing real-time classification results. By integrating the representation power of CNNs with the predictive strength of XGBoost, the proposed method achieves efficient and reliable disaster type detection. This approach has potential applications in disaster monitoring, early warning systems, and emergency response planning, thereby contributing to enhanced situational awareness and risk mitigation.

Keywords: Neural-XGBoost, Disaster Prediction, CNN, XGBoost, Image Classification, Machine Learning.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

Software: Matlab 2022b or above

Hardware:

Operating Systems:

Windows 10
Windows 7 Service Pack 1
Windows Server 2019
Windows Server 2016

Processors:

Minimum: Any Intel or AMD x86-64 processor

Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support

Disk:

Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation

Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space

RAM:

Minimum: 4 GB

Recommended: 8 GB

Learning Outcomes

· Introduction to Matlab

· What is EISPACK & LINPACK

· How to start with MATLAB

· About Matlab language

· Matlab coding skills

· About tools & libraries

· Application Program Interface in Matlab

· About Matlab desktop

· How to use Matlab editor to create M-Files

· Features of Matlab

· Basics on Matlab

· What is an Image/pixel?

· About image formats

· Introduction to Image Processing

· How digital image is formed

· Importing the image via image acquisition tools

· Analyzing and manipulation of image.

· Phases of image processing:

o Acquisition

o Image enhancement

o Image restoration

o Color image processing

o Image compression

o Morphological processing

o Segmentation etc.,

· How to extend our work to another real time applications

· Project development Skills

o Problem analyzing skills

o Problem solving skills

o Creativity and imaginary skills

o Programming skills

o Deployment

o Testing skills

o Debugging skills

o Project presentation skills

o Thesis writing skills