Artificial Intelligence Modelling of Estuarine Crab Habitat Under Salinity Fluctuations

Abstract

The estuarine ecosystem has been known to be significant in providing habitat requirements for crabs in terms of feeding, reproduction, hiding places, and populations' growth. Seasonal variation in salinity due to tidal variation, environmental disturbance, and freshwater discharge may have a significant impact on the presence and suitability of the habitat. Understanding the relation between the variation in salinity and the presence of crabs is significant in ensuring that biodiversity within the coastal area is well conserved. In this research, an ecological modeling framework is provided with the help of artificial intelligence technology to determine the effect of salinity variation on estuarine crab habitats. Machine Learning-Based Evaluation was used as an ecological tool in support to recognize the relation between environmental changes and reactions of crabs. It was found out that salinity is one of the primary factors affecting the crab habitat choice while other environmental conditions including oxygen, vegetation type, and sediment are factors determining habitat stability. The method of prediction proved to be effective in identifying proper habitats of crabs and shows the capabilities of artificial intelligence methods for the analysis of complex ecological relationships. The research emphasizes the significance of combining the process of environmental monitoring and the predictive approach for the management of estuarine biodiversity