Prediction of relative humidity in Santa Clara gallery forests using artificial intelligence

Abstract

he gallery forests on serpentinite soils southeast of Santa Clara are characterized by a very high level of endemism. These forests are the habitat of *Sachsia coronopifolia*, a local endemic plant  that is critically endangered and considered among the 50 most threatened plants in Cuba. The distribution of this species is restricted to these forest patches, which are subject to fragmentation and degradation of forest cover. The objective of this work is to model computational algorithms capable of predicting the future behavior of relative air humidity in the gallery forests southeast of Santa Clara using artificial intelligence. In this regard, the use of time series is considered the most appropriate resource. The results of the ARIMA modeling using artificial intelligence techniques can be useful for identifying and carrying out timely reforestation actions that prevent the negative effects of climate change on these forests.