Although deep learning applicability in various fields of earth sciences is rapidly increasing,
shallow multilayer-perceptron neural networks remain widely used for regression problems …

Due to the spatial-temporal inadequacy of large-scale general circulation models (GCMs),
linking large-scale GCM data with small-scale local climatic data has found great interest. In …

In the present study, for the first time, a new framework is used by combining metaheuristic
algorithms, decomposition and machine learning for flood frequency analysis under climate …

Vegetation is an important link between water, atmosphere and land, and the growth of
vegetation is an important indicator of ecosystem change. Therefore, it is essential to study …

Vehicular traffic noise is the main source of noise pollution in major cities around the globe.
A reliable and accurate method for the estimation of vehicular traffic noise is therefore …

Climate change (CC) and land-use change (LUC) have far-reaching influences on
ecosystem services (ESs), which are key to human well-being. This study aimed to develop …

Generating a long-term high-spatiotemporal resolution global PM 2.5 dataset is of great
significance for environmental management to mitigate the air pollution concerns worldwide …

This research provides a novel methodology for modeling multivariate dependence
structures of meteorological drought characteristics (severity, duration, peak, and interarrival …

In this paper, point prediction and prediction intervals (PIs) of artificial neural network (ANN)
based downscaling for mean monthly precipitation and temperature of two stations (Tabriz …

Purpose The purpose of this study is to identify future changes in weather variables
(precipitation and temperature) due to climate change using different general circulation …