Feature selection is employed to reduce the feature dimensions and computational
complexity by eliminating irrelevant and redundant features. A vast amount of increasing …

Atmospheric extreme events cause severe damage to human societies and ecosystems.
The frequency and intensity of extremes and other associated events are continuously …

When employing remote sensing images, it is challenging to classify vegetation species and
ground objects due to the abundance of wetland vegetation species and the high …

Dust storms represent a major environmental challenge in the Middle East. The southwest
part of Iran is highly affected by dust events transported from neighboring desert regions …

Fine classification of wetland vegetation communities using machine learning algorithm and
high spatial resolution images have attracted increased attention. However, there exist …

Feature Selection (FS) aims to ameliorate the classification rate of dataset models by
selecting only a small set of appropriate features from the initial range of features. In …

Abstract The Water Quality Index (WQI) is a primary metric used to evaluate and categorize
surface water quality which plays a crucial role in the management of fresh water resources …

Nowadays, dried lakes have turned into important dust sources with serious environmental,
climatic and socio-economic impacts. In this study, climatic, terrestrial and anthropogenic …

Remote sensing analyses frequently use feature selection methods to remove non-
beneficial feature variables from the input data, which often improve classification accuracy …

Droughts pose a severe environmental risk in countries that rely heavily on agriculture,
resulting in heightened levels of concern regarding food security and livelihood …