HAECNN: An Intensity Analysis and Classification of Natural Disasters Using Hybrid Auto-Encoder - Convolutional Neural Network Model for Unprecedented Accuracy

Natural catastrophes are a serious threat to the environment, human lives, and property. Effective disaster management and response depend on the precise and timely determination of these occurrences' intensity and classification. In this work, we present a unique method for the analysis and categorization of natural disasters: the Hybrid Auto-Encoder - Convolutional Neural Network (HAECNN). By combining the strengths of convolutional neural networks with auto-encoders, HAECNN is able to classify and measure disaster intensity with previously unheard-of accuracy.HAECNN's auto-encoder part is in charge of autonomously extracting key features from unprocessed inputs like pictures, sensor signals, or satellite imagery. By capturing important details about the calamity, these extracted features strengthen and improve the model's ability to handle a variety of data sources. After the features are recovered, the convolutional neural network component analyses the data to identify spatial patterns and relationships.We carried out in-depth tests on a broad dataset comprising many kinds of natural catastrophes, such as hurricanes, earthquakes, floods, and wildfires, in order to validate the performance of HAECNN. With accuracy rate of over 95% in disaster classification and accurate intensity estimation, our model surpassed other available techniques. Because it can successfully adapt and generalise, HAECNN's outstanding accuracy is ascribed to its ability to utilise the intrinsic data structures and relationships present in many disaster types.For the analysis and categorization of natural disasters, the HAECNN model is a viable option that enhances early warning systems, timely reaction plans, and disaster planning. It is an invaluable tool for governments, first responders, and researchers trying to lessen the effects of natural disasters on the environment and society because of its unmatched accuracy and adaptability.