Integrating a novel algorithm in assessing the impact of floods on the genetic diversity of a high commercial value fish (Cyprinidae: Spinibarbus sp.) in Lang Son province of Vietnam

Floods, which occur when the amount of precipitation surpasses the capacity of an area to drain it adequately, have detrimental consequences on the survival and future generations of fishes. However, few works have reported the prediction of this natural phenomenon in a relation to certain fish species, especially in fast-flowing rivers. In the specific context of the northern mountainous provinces of Vietnam, where the Spinibarbus sp. fish species resides, it has been observed through the current study that the fish population in Lang Son exhibits the lowest genetic diversity and genetic distance. Consequently, the population of Spinibarbus sp. in Lang Son shows a heightened susceptibility to floods, resulting in reduction in population size and compromised population resilience. In order to provide decision support information for managers, conservationists, and researchers, we have employed a genetic algorithm-support vector machine regression (GA-SVR) predictive model to map flood vulnerability using thirteen dependent variables. The study findings have unveiled a significant negative correlation between flood-sensitive regions and genetic diversity. These discoveries emphasize the significance of considering the impact of floods on the genetic diversity of Spinibarbus sp. in Lang Son through flood vulnerability mapping. This underscores the value of establishing a comprehensive framework based on the GA-SVR algorithm for early flood detection, thereby facilitating the implementation of effective measures to minimize damages and conserve this commercial fish species.