CORAL HEALTH ASSESSMENT AND SPECIES DISTRIBUTION MAPS USING LSTM

Abstract

The evaluation and mapping of coral reef health and species distribution are increasingly more crucial as climate alternate and environmental stressors impact marine ecosystems. This evaluate consolidates latest advancements in using deep gaining knowledge of fashions, mainly Long Short-Term Memory (LSTM) networks, to beautify the accuracy and scalability of coral reef tracking. The software of LSTM models, including convolutional LSTM (ConvLSTM), demonstrates tremendous capacity in predicting sea surface temperature (SST) versions, important for forecasting coral bleaching occasions and information coral fitness dynamics. Our review highlights the effectiveness of LSTM-based totally models in spatio-temporal generalization, addressing limitations of previous gadget-mastering procedures through improving prediction accuracy and generalizability throughout distinct coral reef environments. We also observe the mixing of LSTM models with far off sensing technologies for massive-scale benthic composition mapping and species distribution, revealing the capability of these fashions to seriously decorate coral reef control and conservation techniques. Additionally, improvements in predictive fashions for particulate count (PM2.5) forecasting and water first-rate assessment are explored, underscoring the wider implications of LSTM and different machine gaining knowledge of techniques in environmental tracking. The evaluate concludes that leveraging LSTM networks and integrating them with remote sensing information gives promising avenues for improved coral fitness assessment and species distribution mapping, essential for the sustainable control of marine ecosystems within the face of ongoing environmental modifications.