Reference health
Convolutional Neural Network-Long Short-Term Memory-Bidirectional Gated Recurrent Unit Forecasting System for Multi-Step Significant Wave Height Prediction in Australia's Wave Energy Region
31/31 checkable references clean · checked 2026-08-29
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
43 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 31 checked references that resolve
resolves10.1111/j.1467-9523.2006.00303.x
The impacts of climate change on the risk of natural disasters
resolves10.1016/j.jnlssr.2020.06.009
Impact of Australia's catastrophic 2019/20 bushfire season on communities and environment. Retrospective analysis and current trends
resolves10.1016/j.esr.2016.10.003
Electricity generation technology options under the greenhouse gases mitigation scenario: Case study of Cameroon
resolves10.1007/s13181-011-0203-1
Clearing the Air: A Review of the Effects of Particulate Matter Air Pollution on Human Health
resolves10.1038/s41586-018-0263-3
Robust relationship between air quality and infant mortality in Africa
resolves10.1016/j.jeem.2021.102448
Pandemic meets pollution: Poor air quality increases deaths by COVID-19
resolves10.1016/j.oceaneng.2009.08.008
Comparison between M5′ model tree and neural networks for prediction of significant wave height in Lake Superior
resolves10.1016/j.engappai.2015.03.012
Significant wave height and energy flux range forecast with machine learning classifiers
resolves10.1175/JCLI-D-14-00470.1
Marine Wind and Wave Height Trends at Different ERA-Interim Forecast Ranges
resolves10.1016/j.rser.2019.01.014
Significant wave height forecasting via an extreme learning machine model integrated with improved complete ensemble empirical mode decomposition
resolves10.1007/s12517-020-05498-1
Predicting lake wave height based on regression classification and multi input–single output soft computing models
resolves10.1016/j.oceaneng.2010.07.009
Significant wave height forecasting using wavelet fuzzy logic approach
resolves10.1016/j.rser.2015.12.253
Computational intelligence in wave energy: Comprehensive review and case study
resolves10.1016/j.oceaneng.2020.107777
A wavelet - Particle swarm optimization - Extreme learning machine hybrid modeling for significant wave height prediction
resolves10.1016/j.oceaneng.2016.05.049
A hybrid EMD-SVR model for the short-term prediction of significant wave height
resolves10.1016/j.enconman.2017.01.022
A combined model based on CEEMDAN and modified flower pollination algorithm for wind speed forecasting
resolves10.3390/rs13040554
Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data
resolves10.1109/ACCESS.2020.3039002
Deep Air Quality Forecasts: Suspended Particulate Matter Modeling With Convolutional Neural and Long Short-Term Memory Networks
resolves10.25080/Majora-8b375195-003
Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms
resolves10.1016/j.jhydrol.2021.126800
Long short-term memory neural network (LSTM-NN) for aquifer level time series forecasting using in-situ piezometric observations
resolves10.1016/j.amc.2021.126332
A deep learning approach for computations of exposure profiles for high-dimensional Bermudan options
resolves10.1016/j.envsoft.2010.03.031
Raising the bar? – The challenges of evaluating the outcomes of environmental modelling and software
resolves10.1016/j.jhydrol.2009.08.003
Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling
resolves10.1016/0022-1694(70)90255-6
River flow forecasting through conceptual models part I — A discussion of principles
resolves10.1080/19942060.2020.1773932
Prediction of significant wave height; comparison between nested grid numerical model, and machine learning models of artificial neural networks, extreme learning and support vector machines
The 43 references without a DOI — listed, not checked
no DOI — not checkedref1
no DOI — not checkedref2
no DOI — not checkedref3
no DOI — not checkedref6
no DOI — not checkedref12
no DOI — not checkedref13
no DOI — not checkedAn EEMD-BiLSTM Algorithm Integrated with Boruta Random Forest Optimiser for Significant Wave Height Forecasting along Coastal Areas of Queensland
no DOI — not checkedref16
no DOI — not checkedref17
no DOI — not checkedref18
no DOI — not checkedApplication of the Ensemble Kalman Filter to a high-resolution wave forecasting model for wave height forecast in coastal areas
no DOI — not checkedWave Height Forecast Using Self-Organization Algorithm Model
no DOI — not checkedNearshore wave predictions using data mining techniques during typhoons: a case study near Taiwan's northeastern coast
no DOI — not checkedNear real-time significant wave height forecasting with hybridized multiple linear regression algorithms
no DOI — not checkedref30
no DOI — not checkedref32
no DOI — not checkedA two-step hybrid system towards optimized wave height forecasts
no DOI — not checkedref36
no DOI — not checkedA complete ensemble empirical mode decomposition with adaptive noise
no DOI — not checkedIntelligent optimized deep learning hybrid models of neuro wavelet, Fourier Series and Recurrent Kalman Filter for tidal currents constitutions forecasting
no DOI — not checkedA novel hybrid deep learning model for sugar price forecasting based on time series decomposition
no DOI — not checkedAn integrated long-short term memory algorithm for predicting polar westerlies wave height
no DOI — not checkedref43
no DOI — not checkedref44
no DOI — not checkedA hybrid deep learning model for short-term PV power forecasting
no DOI — not checkedref47
no DOI — not checkedSocial multimedia security and suspicious activity detection in SDN using hybrid deep learning technique
no DOI — not checkedShort-term load forecasting in smart grid: A combined CNN and K-means clustering approach
no DOI — not checkedDeep neural networks for energy load forecasting
no DOI — not checkedDeep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms
no DOI — not checkedref52
no DOI — not checkedBiGRU-CNN neural network applied to short-term electric load forecasting
no DOI — not checkedref55
no DOI — not checkedref56
no DOI — not checkedDeep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data
no DOI — not checkedref58
no DOI — not checkedAutomatic and non-invasive Parkinson's disease diagnosis and severity rating using LSTM network
no DOI — not checkedref65
no DOI — not checkedref66
no DOI — not checkedref67
no DOI — not checkedref68
no DOI — not checkedref72
no DOI — not checkedref73
What this badge says. CiteStamped means the CHECKABLE references of this
work were clean at the dated check: each resolved to a known work in a public registry, and none
carried a retraction notice at that time. It says nothing about the quality, findings,
or importance of the work itself, and nothing about references deposited without a DOI.
checked 2026-08-29 — re-checked daily as this page is visited; titles and statuses come from Crossref and DataCite and are not part of the signed record
Embed this badge
Both snippets point at the live badge image and link back to this page. The badge re-renders from the daily check, so an embed never goes stale by more than a day of visits.
<a href="https://citestamp.com/citestamped/10.2139/ssrn.4500555"><img src="https://citestamp.com/citestamped/10.2139/ssrn.4500555/badge.svg" alt="CiteStamped reference-health badge" width="460" height="64"></a>
[](https://citestamp.com/citestamped/10.2139/ssrn.4500555)