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Wavelet analyses of neural networks based river discharge decomposition

https://doi.org/10.1002/hyp.13726
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Predictability of Hydrological Systems Using the Wavelet Transformation: Application to Drought Prediction
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Applications of Wavelet Transform Technique in Hydrology—A Brief Review
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Input Selection of Wavelet-Coupled Neural Network Models for Rainfall-Runoff Modelling
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Artificial neural network for modeling nitrate pollution of groundwater in marginal area of Zayandeh-rood River, Isfahan, Iran
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Modelling of impact of water quality on infiltration rate of soil by random forest regression
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Water quality modelling using artificial neural network and multivariate statistical techniques
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River Water Quality Modelling Using Artificial Neural Network Technique
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A review on the applications of wavelet transform in hydrology time series analysis
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Artificial neural network modeling of the river water quality—A case study
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Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review
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Comparative study of different wavelet based neural network models for rainfall–runoff modeling
inferredcites →https://doi.org/10.1016/j.jhydrol.2016.11.033openalex-snapshot
An Emotional ANN (EANN) approach to modeling rainfall-runoff process
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The role of vegetation, soils, and precipitation on water storage and hydrological services in Andean Páramo catchments
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Explaining nonlinear classification decisions with deep Taylor decomposition
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Spatially distributed hydro-chemical data with temporally high-resolution is needed to adequately assess the hydrological functioning of headwater catchments
inferredcites →https://doi.org/10.1016/s0022-1694(98)00242-xopenalex-snapshot
Short term streamflow forecasting using artificial neural networks
inferredcites →https://doi.org/10.1023/b:warm.0000024727.94701.12openalex-snapshot
River Flow Forecasting using Recurrent Neural Networks
inferredcites →https://doi.org/10.1029/2018jd028455openalex-snapshot
Identifying Sustained Drought Anomalies in Hydrological Records: A Wavelet Approach
inferredcites →https://doi.org/10.1029/95wr01955openalex-snapshot
Artificial Neural Network Modeling of the Rainfall‐Runoff Process
inferredcites →https://doi.org/10.1061/(asce)0733-9496(2009)135:5(314)openalex-snapshot
ANN-GA-Based Model for Multiple Objective Management of Coastal Aquifers
inferredcites →https://doi.org/10.1061/(asce)he.1943-5584.0000587openalex-snapshot
Two Semidistributed ANN-Based Models for Estimation of Suspended Sediment Load
inferredcites →https://doi.org/10.1080/02626667.2016.1154151openalex-snapshot
Comparison of an artificial neural network and a conceptual rainfall–runoff model in the simulation of ephemeral streamflow
inferredcites →https://doi.org/10.1090/qam/10666openalex-snapshot
A method for the solution of certain non-linear problems in least squares
inferredcites →https://doi.org/10.1109/access.2018.2870052openalex-snapshot
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
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Neural network modeling of hydrological systems: A review of implementation techniques
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An Algorithm for Least-Squares Estimation of Nonlinear Parameters
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inferredcites →https://doi.org/10.1175/1520-0477(1995)076<2391:csduwt>2.0.co;2openalex-snapshot
Climate Signal Detection Using Wavelet Transform: How to Make a Time Series Sing
inferredcites →https://doi.org/10.1175/1520-0477(1998)079<0061:apgtwa>2.0.co;2openalex-snapshot
A Practical Guide to Wavelet Analysis
inferredcites →https://doi.org/10.1175/jhm-d-14-0135.1openalex-snapshot
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inferredcites →https://doi.org/10.1201/9780203024119.ch2openalex-snapshot
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inferredcites →https://doi.org/10.2134/jeq2003.374aopenalex-snapshot
Mathematical Models of Small Watershed Hydrology and Applications
inferredcites →https://doi.org/10.2166/nh.2016.202openalex-snapshot
Groundwater budget forecasting, using hybrid wavelet-ANN-GP modelling: a case study of Azarshahr Plain, East Azerbaijan, Iran
inferredcites →https://doi.org/10.5194/hess-13-1619-2009openalex-snapshot
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inferredcites →https://doi.org/10.5194/hess-20-2987-2016openalex-snapshot
Insights into the water mean transit time in a high-elevation tropical ecosystem
inferredcites →https://doi.org/10.5194/npg-11-561-2004openalex-snapshot
Application of the cross wavelet transform and wavelet coherence to geophysical time series

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Spatial and Temporal Forecasting of Groundwater Levels
inferred← citeshttps://doi.org/10.1002/clen.202000381openalex-snapshot
Comparative Study of Different Types of Hydrological Models Applied to Hydrological Simulation
inferred← citeshttps://doi.org/10.1002/hyp.14420openalex-snapshot
Identification of the controlling factors for hydrological responses by artificial neural networks
inferred← citeshttps://doi.org/10.1007/s11356-022-23686-2openalex-snapshot
Groundwater level response identification by hybrid wavelet–machine learning conjunction models using meteorological data
inferred← citeshttps://doi.org/10.1007/s11356-023-26202-2openalex-snapshot
Grade evaluation of black-odorous urban rivers in the Greater Bay Area of China using an improved back propagation (BP) neural network
inferred← citeshttps://doi.org/10.1007/s11600-022-00964-8openalex-snapshot
A hybrid wavelet–machine learning model for qanat water flow prediction
inferred← citeshttps://doi.org/10.1016/j.agwat.2023.108161openalex-snapshot
Improving the precision of monthly runoff prediction using the combined non-stationary methods in an oasis irrigation area
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Stacking ensemble learning models for daily runoff prediction using 1D and 2D CNNs
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Combining two-stage decomposition based machine learning methods for annual runoff forecasting
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inferred← citeshttps://doi.org/10.1029/2021wr031412openalex-snapshot
Improved Understanding of How Catchment Properties Control Hydrological Partitioning Through Machine Learning
inferred← citeshttps://doi.org/10.1080/02626667.2025.2506745openalex-snapshot
Spectral-based indices for assessing flow regime alterations in dammed rivers: a global perspective
inferred← citeshttps://doi.org/10.21203/rs.3.rs-4033404/v1openalex-snapshot
Comparison of LSTM, Auto-LSTM, and MetroMan on SWOT-like Data
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The study on the time lag of water level in the Three Gorges Reservoir under the regulation processes
inferred← citeshttps://doi.org/10.3389/frwa.2023.1233899openalex-snapshot
Integrating geographic data and the SCS-CN method with LSTM networks for enhanced runoff forecasting in a complex mountain basin
inferred← citeshttps://doi.org/10.3390/app12020647openalex-snapshot
Prediction of River Sediment Transport Based on Wavelet Transform and Neural Network Model
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Evolution of Flood Regulation Capacity for a Large Shallow Retention Lake: Characterization, Mechanism, and Impacts
inferred← citeshttps://doi.org/10.3390/w15152838openalex-snapshot
Study on Response Process and Time Delay Effect of Groundwater Dynamic in Northeastern Margin of Tibetan Plateau
inferred← citeshttps://doi.org/10.3390/w15162968openalex-snapshot
Evaluation of Methods for Estimating Long-Term Flow Fluctuations Using Frequency Characteristics from Wavelet Analysis
inferred← citeshttps://doi.org/10.3390/w16010026openalex-snapshot
Transformative Trends in Runoff and Sediment Dynamics and Their Influential Drivers in the Wuding River Basin of the Yellow River: A Comprehensive Analysis from 1960 to 2020
inferred← citeshttps://doi.org/10.3934/geosci.2024027openalex-snapshot
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