Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3610 2018 2018 Postdoctoral Fellow, Perdana University, Kuala Lumpur 50490, Malaysia; Amity University Madhya Pradesh, Gwalior 474005, India Daniel Daniel Faculty of Applied Science and Technology, Perdana University, Kuala Lumpur 50490, Malaysia Azween Abdullah Open & Dist. Learning Centre, Perdana University, Kuala Lumpur 50490, Malaysia Valliappan Raju Weather monitoring is a vital challenge in dissimilar areas of applications such as military missions, higher precision agriculture, outdoor entertainment and recreation, industrial manufacture, and logistics. The most vital application is natural weather disaster monitoring. Weather change has made stronger an occurrence of natural disasters all over the world. More extreme climate events have been experienced for the past few years, like lower and higher temperatures, sturdy winds in humid cyclones, heavy rains, and intensified lack. Therefore, at present, remote sensing imagery (RSI) analysis is necessary in the field of ecological and weather monitoring mainly for the application of identifying and handling a natural climate disaster. To upsurge the accuracy of detection, machine learning (ML) and deep learning (DL) systems were applied to enhance the efficacy of removing features and help to perceive large-scale losses like landslides, earthquakes, and floods. In this manuscript, we design and develop a Weather Disaster Detection Model Using Zebra Optimization Algorithm with Ensemble Learning on Remote Sensing Images (WDDZOA-ELRSI) technique. The proposed WDDZOA-ELRSI model's main intention is to improve the detection model of weather disasters using state-of-the-art DL methods. Initially, the bilateral filter (BF) method is employed in the image pre-processing stage to eliminate the unwanted noise from input data. Furthermore, the feature extraction method executes GoogleNet technique to transform raw data into a reduced set of relevant features. For the classification process, the ensemble of deep learning models such as conditional variational autoencoder (CVAE), graph convolutional network (GCN), and Elman recurrent neural network (ERNN) have been deployed. Eventually, the zebra optimization algorithm (ZOA)-based hyperparameter tuning procedure has been achieved to improve the detection outcomes of ensemble models. The simulation analysis of the WDDZOA-ELRSI system is verified on a benchmark image dataset and the outcomes were evaluated under numerous measures. The simulation outcome emphasized the enhancement of the WDDZOA-ELRSI model in the weather disaster detection process 2025 2025 222 234 10.54216/FPA.190117 https://www.americaspg.com/articleinfo/3/show/3610