The dynamic structure of climate is governed by changes in precipitation and temperature
and can be studied by time series analysis of these factors. This paper describes …

Relative humidity plays an important role in climate change and global warming, making it a
research area of greater concern in recent decades. The present study attempted to …

Time-series models have vast advantages in the study of dynamic systems, especially if the
aims are to determine structure and stability of population or finding regime shifts in dynamic …

The mounting impacts of climate change on infrastructure demand proactive adaptation
strategies to ensure long-term resilience. This study investigates the effects of predicted …

In this study, a hybrid Machine Learning (ML) approach is proposed for Relative Humidity
(RH) prediction with a combination of Empirical Mode Decomposition (EMD) to improve the …

A generalized structural time-series modeling framework was used to analyze the monthly
records of mean temperature, one of the most important environmental parameters, using …

Stochastic models (time series models) have been proposed as one technique to generate
scenarios of future climate change. Precipitation, temperature and evaporation are among …

The main goal of this paper to evaluate the performance of the proposed long short-term
memory (LSTM)-basis relative humidity (RH) prediction model. Computational physics …

Deep Learning (DL) models can make short-and long-term predictions in just a few seconds,
beyond the capabilities of traditional physical models. However, the capabilities of different …

Abstract The Karun River is the longest (950 km) in Iran and drains a large 65,230 km2
watershed. The river is polluted by the oil and steel industry, municipal and agricultural …