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Multi-depth daily soil temperature modeling: meteorological variables or time series?
This study presents the first-time application of a novel emotional neural network (ENN) for soil temperature modeling. Two scenarios were considered...
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Modeling trends and periodic components in geodetic time series: a unified approach
Geodetic time series are usually modeled with a deterministic approach that includes trend, annual, and semiannual periodic components having...
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Modeling and forecasting rainfall patterns in India: a time series analysis with XGBoost algorithm
This study utilizes time series analysis and machine learning techniques to model and forecast rainfall patterns across different seasons in India....
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Modeling seasonal oscillations in GNSS time series with Complementary Ensemble Empirical Mode Decomposition
We present the modeling of annual and semiannual signals in position time series of GNSS stations. The employed method is the Complementary Ensemble...
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Relationships between River and Groundwater Flow in an Alluvial Plain by Time Series Analysis and Numerical Modeling
Alluvial plains represent hydrological systems where the aquifer and the associated stream network are in hydraulic communication. In many instances,...
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Reconstruction of geodetic time series with missing data and time-varying seasonal signals using Gaussian process for machine learning
Seasonal signals in satellite geodesy time series are mainly derived from a number of loading sources, such as atmospheric pressure and hydrological...
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Modeling and prediction of land use land cover change dynamics based on spatio-temporal analysis of optical and radar time series of remotely sensed images
Land use / land cover (LULC) has changed dramatically in recent years, especially in areas that have experienced severe climate change and population...
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Anatomy of the spatiotemporally correlated noise in GNSS station position time series
Global Navigation Satellite Systems (GNSS) enable the determination of station displacements, which are essential to understanding geophysical...
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Bivariate Time Series Analysis
Analysis of bivariate time series is necessary for detecting teleconnections and reconstructing climate but it is almost always done in natural...
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Modeling of rainfall time series using NAR and ARIMA model over western Himalaya, India
The high Himalayas in northern India are an essential source of climate generation and maintenance over the entire northern belt of the Indian...
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Measurement of surface deformation related to the December 2018 Mt. Etna eruption using time-series interferometry and magma modeling for hazard zone map**
Mount Etna has erupted several times since it was first formed. Recently, Mount Etna began erupting again over 24–27 December 2018. Because it erupts...
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Analysis of Scalar Time Series
The basics of theory of random processes including the concepts of stationarity, ergodicity, and linearity are given in simple form to help user to...
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Non-stationary modeling of seasonal precipitation series in Turkey: estimating the plausible range of seasonal extremes
It is increasingly recognized that the assumption of stationarity is not always appropriate for estimating return periods from meteorological time...
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Gap-filling missing data in time series using the correlation matrix method of multiple time series in Asadabad Plain, Iran
Groundwater resources are crucial sources of water supply, and preserving the quality of these resources is an undeniable necessity. On the other...
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Numerical Simulation of the Time Series of Bioclimatic Indices in the Russian Arctic Based on a Stochastic Weather Generator
AbstractThe paper proposes an approach to the numerical stochastic modeling of the time series of the wind chill index and equivalent effective...
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Neural network based system in evapotranspiration time series prediction
Evapotranspiration is a very important process of the water cycle. Thus, the ability to model and understand evolution of this process is very...
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Strategies to remove hydrological effects in continuous gravity time series
Multi-annual gravity time series offer a unique, noninvasive way to monitor mass redistributions within the Earth. However, for non-hydrological...
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A Novel Smoothing-Based Deep Learning Time-Series Approach for Daily Suspended Sediment Load Prediction
Precise assessment of suspended sediment load (SSL) is vital for many applications in hydrological modeling and hydraulic engineering. In this study,...
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Modeling Time-Dependent Uniaxial Compressive Behaviors of an Artificial Frozen Sandy Clay at Different Temperatures
Extensive experimental studies have demonstrated the time-dependent mechanical behaviors of frozen soil. Nonetheless, limited studies are focusing on...
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Time Series Forecasting of Indian Coal Mines Fatal Accidents
AbstractThe present study analyzes the fatal accident occurrences of seventy years from 1951 to 2020 in Indian coal mines. The autoregressive...
