Computer simulation experiments are essential to modern scientific discovery, whether that
be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are …

Daily streamflow forecasting through data-driven approaches is traditionally performed
using a single machine learning algorithm. Existing applications are mostly restricted to …

Research within the field of hydrology often focuses on the statistical problem of comparing
stochastic to machine learning (ML) forecasting methods. The performed comparisons are …

One of the stages in cluster analysis, carried out on the basis of metric data (interval, ratio), is
the choice of variable normalization method. This paper presents the proposal of two …

Our goal is to provide an easy-to-follow applied book on semiparametric regression
methods using R. Semiparametric regression has a large literature, but much of it is geared …

Nutrients such as phosphorus and nitrogen lead to extensive growth of harmful algae in
lakes and reservoirs, which results in eutrophication. The driving mechanism of primary …

The water adsorption properties of Achira biscuits were investigated using a mathematical
model-based strategy to understand the relationship between water activity and moisture …

Increasingly, applications of machine learning techniques for digital soil map** (DSM) are
being used for different soil map** purposes. Considering the variety of models available …

In aviation, fog is a severe phenomenon, causing difficulties in airport traffic management;
thus, accurate fog forecasting is always appreciated. The current paper presents a fog …

Fatigue is the most common symptom among multiple sclerosis (MS) patients and severely
affects the quality of life. We investigate how perceived fatigue can be predicted using …