An Assessment of Geological Hazard Management using Dynamically Stabilized Recurrent Neural Network and Beluga Whale Optimization Algorithm

Mountainous region geological hazards are a leading cause of natural disasters, resulting in significant human and economic losses. Regional topography, landforms, lithology, plant life, geological circumstances, and meteorology all have a significant impact on their creation. Gansu, situated in the interior of Northwestern China, features Lanzhou as its capital and primary urban center, positioned in the southeast of the province. Geological risks, particularly landslides, mudslides, and avalanches, present significant challenges to Gansu Province. Consequently, local authorities are actively devising customized strategies to mitigate these hazards and foster sustainable development. In this research work, an Assessment of Geological Hazard Management using Dynamically Stabilized Recurrent Neural Network and Beluga Whale Optimization Algorithm (AGL-HM-DSRNN-BWOA) is proposed. Initially, the input raster data are gathered from the Normalised Difference Vegetation Index (NDVI) dataset. The input raster data is then pre-processed using Adaptive Actor-Critic Bilateral Filter (A2CBF) to reduce noises and increase the overall quality of the raster data. To classify the geological hazards, the pre-processed raster data are fed into a neural network named DSRNN. The geological hazard is accurately categorized as low risk, medium-low risk, medium-high risk, high risk using proposed DSRNN. In general, DSRNN does not express some adaption of optimization strategies for determining optimal parameters to promise exact classification for managing geological hazard by assessment. Therefore, Beluga Whale Optimization Algorithm (BWOA) is proposed to enhance weight parameter of DSRNN classifier, which precisely assess for managing the geological hazards. The efficiency of the proposed AGL-HM-DSRNN-BWOA approach is evaluated using a number of performance criteria, including accuracy, sensitivity, specificity, ROC, mean square error, root mean square error, mean absolute error. The proposed AGL-HM-DSRNN-BWOA method attains 22.36%, 25.42% and 18.17% higher accuracy, 21.26%, 15.42% and 19.27% higher sensitivity, 28.36%, 25.32% and 28.27% higher F-measure compared with existing methods, such as the Risk assessment and its influencing factors examination of geological hazards in typical mountain environment (RA-IFA-GH-TME), Feasibility study of land cover categorization under normalized difference vegetation index for landslide risk assessment(LCC-NDVI-LRA), and Multiple hazard exposure mapping under machine learning for Salzburg, Austria (MH-EM-SSA-ML) respectively.