Recent advances in wavelet analyses: Part 1. A review of concepts
D Labat - Journal of Hydrology, 2005 - Elsevier
This contribution provides a review of the most recent wavelet applications in the field of
earth sciences and is devoted to introducing and illustrating new wavelet analysis methods …
earth sciences and is devoted to introducing and illustrating new wavelet analysis methods …
Forecasting river water temperature time series using a wavelet–neural network hybrid modelling approach
Accurate and reliable water temperature forecasting models can help in environmental
impact assessment as well as in effective fisheries management in river systems. In this …
impact assessment as well as in effective fisheries management in river systems. In this …
[HTML][HTML] Variational mode decomposition based random forest model for solar radiation forecasting: new emerging machine learning technology
Forecasting of solar radiation (Radn) can provide an insight vision for the amount of green
and friendly energy sources. Owing to the non-linearity and non-stationarity challenges …
and friendly energy sources. Owing to the non-linearity and non-stationarity challenges …
Comparative study of different wavelet based neural network models for rainfall–runoff modeling
The use of wavelet transformation in rainfall–runoff modeling has become popular because
of its ability to simultaneously deal with both the spectral and the temporal information …
of its ability to simultaneously deal with both the spectral and the temporal information …
Analysis of geophysical time series using discrete wavelet transforms: An overview
DB Percival - Nonlinear Time Series Analysis in the Geosciences …, 2008 - Springer
Discrete wavelet transforms (DWTs) are mathematical tools that are useful for analyzing
geophysical time series. The basic idea is to transform a time series into coefficients …
geophysical time series. The basic idea is to transform a time series into coefficients …
Division-based rainfall-runoff simulations with BP neural networks and Xinanjiang model
The application of artificial neural network (ANN) to rainfall-runoff simulations has provided
promising results in recent years. However, it is difficult to obtain satisfying results by using …
promising results in recent years. However, it is difficult to obtain satisfying results by using …
Application of nonlinear time series and machine learning algorithms for forecasting groundwater flooding in a lowland karst area
In karst limestone areas interactions between ground and surface waters can be frequent,
particularly in low lying areas, linked to the unique hydrogeological dynamics of that bedrock …
particularly in low lying areas, linked to the unique hydrogeological dynamics of that bedrock …
Integrated approach to model decomposed flow hydrograph using artificial neural network and conceptual techniques
A Jain, S Srinivasulu - Journal of Hydrology, 2006 - Elsevier
This paper presents the findings of a study aimed at decomposing a flow hydrograph into
different segments based on physical concepts in a catchment, and modelling different …
different segments based on physical concepts in a catchment, and modelling different …
Human amplified changes in precipitation–runoff patterns in large river basins of the Midwestern United States
SA Kelly, Z Takbiri, P Belmont… - Hydrology and Earth …, 2017 - hess.copernicus.org
Complete transformations of land cover from prairie, wetlands, and hardwood forests to row
crop agriculture and urban centers are thought to have caused profound changes in …
crop agriculture and urban centers are thought to have caused profound changes in …
SNO KARST: A French network of observatories for the multidisciplinary study of critical zone processes in karst watersheds and aquifers
Core Ideas SNO KARST is dedicated to the study of karst functioning. Hydrodynamics and
geochemistry are measured at springs and in karst compartments. Process sampling was …
geochemistry are measured at springs and in karst compartments. Process sampling was …