Commercial success of big data has led to speculation that big-data-like reasoning could
partly replace theory-based approaches in science. Big data typically has been applied to …

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 …

In this paper we proposed a novel classification system to distinguish among elderly
subjects with Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal …

Effective selection of climate predictors is a fundamental aspect of climate modeling
research. Predictor Screening (PS) plays a crucial role in identifying regional climate drivers …

This article is the second of a series of two articles. In the first article, two models were
developed with National Centers for Environmental Prediction/National Center for …

Climate community frequently uses gridded reanalysis data sets in their climate change
impact studies. However, these studies for a region yield more realistic results depending on …

Accurate high-spatial-resolution precipitation is significantly important in hydrological and
meteorological modelling, especially in rain-gauge-sparse areas. Some methods and …

One of the main methods for determining coagulant dosage (CD) is the jar test. However,
this method is expensive, time-consuming, and requires laboratory equipment. In this …

Abstract Combined Sewer Overflow (CSO) infrastructure are conventionally designed based
on historical climate data. Yet, variability in rainfall intensities and patterns caused by climate …

Precipitation, especially in regions dominated by the Mediterranean climate, is one of the
most critical parameters of the hydrological cycle and the environment affected by climate …