Artificial intelligence and machine learning are ubiquitous in the domain of Earth
Observation (EO) and Remote Sensing. Congruent to their success in the domain of …

To encourage the consumption of crumb rubber (CR), gene expression programming (GEP)
has been exercised in this paper to establish empirical models for estimation of mechanical …

Climate change has caused droughts to increase in frequency and severity worldwide,
which has attracted scientists to create drought prediction models to mitigate the impacts of …

Drought is a natural hazard which affects ecosystems in the eastern Mediterranean.
However, limited historical data for drought monitoring and forecasting are available in the …

In recent years, black-box machine learning approaches have become a dominant modeling
paradigm for knowledge extraction in remote sensing. Despite the potential benefits of …

With the emergence of multisource data and the development of cloud computing platforms,
accurate prediction of event-scale dust source regions based on machine learning (ML) …

Identifying the areas susceptible to dust storm formation is one effective way of dealing with
this destructive environmental phenomenon. This study is the first attempt to employ the …

In this paper, we develop a novel regularization method for deep neural networks by
penalizing the trace of Hessian. This regularizer is motivated by a recent guarantee bound of …

Dust source susceptibility modeling and map** is the first step in managing the impacts of
dust on environmental systems and human health. In this study, satellite products and …

The selection of appropriate strategies is required to mitigate drought effects in farmlands.
Prioritizing the severity of the water deficit in vineyards is further essential to take early …