A surrogate weighted mean ensemble method to reduce the uncertainty at a regional scale for the calculation of potential evapotranspiration

Kim, Junhwan; Lee, Byun-Woo; Hoogenboom, Gerrit; Kim, Kwang S.

doi:10.1038/s41598-020-57466-0

Cited by 16 publications

(15 citation statements)

References 38 publications

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“…The filtered data set was initially preprocessed (eg, removing links, hashtags, and stop words) and a new hierarchical hybrid ensemble–based AI model was developed for thematic sentiment analysis. This utilized an average weighting ensemble [ 19 ] of 2 lexicon-based methods: Valence Aware Dictionary for Sentiment Reasoning (VADER) [ 20 ] and TextBlob [ 21 ]. These were combined with a pretrained DL-based model, Bidirectional Encoder Representations from Transformers (BERT) [ 22 ], using a rule-based ensemble method ( Figure 1 ).…”

Section: Methodsmentioning

confidence: 99%

Artificial Intelligence–Enabled Analysis of Public Attitudes on Facebook and Twitter Toward COVID-19 Vaccines in the United Kingdom and the United States: Observational Study

Hussain¹,

Tahir²,

Hussain³

et al. 2021

J Med Internet Res

244

204

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Background Global efforts toward the development and deployment of a vaccine for COVID-19 are rapidly advancing. To achieve herd immunity, widespread administration of vaccines is required, which necessitates significant cooperation from the general public. As such, it is crucial that governments and public health agencies understand public sentiments toward vaccines, which can help guide educational campaigns and other targeted policy interventions. Objective The aim of this study was to develop and apply an artificial intelligence–based approach to analyze public sentiments on social media in the United Kingdom and the United States toward COVID-19 vaccines to better understand the public attitude and concerns regarding COVID-19 vaccines. Methods Over 300,000 social media posts related to COVID-19 vaccines were extracted, including 23,571 Facebook posts from the United Kingdom and 144,864 from the United States, along with 40,268 tweets from the United Kingdom and 98,385 from the United States from March 1 to November 22, 2020. We used natural language processing and deep learning–based techniques to predict average sentiments, sentiment trends, and topics of discussion. These factors were analyzed longitudinally and geospatially, and manual reading of randomly selected posts on points of interest helped identify underlying themes and validated insights from the analysis. Results Overall averaged positive, negative, and neutral sentiments were at 58%, 22%, and 17% in the United Kingdom, compared to 56%, 24%, and 18% in the United States, respectively. Public optimism over vaccine development, effectiveness, and trials as well as concerns over their safety, economic viability, and corporation control were identified. We compared our findings to those of nationwide surveys in both countries and found them to correlate broadly. Conclusions Artificial intelligence–enabled social media analysis should be considered for adoption by institutions and governments alongside surveys and other conventional methods of assessing public attitude. Such analyses could enable real-time assessment, at scale, of public confidence and trust in COVID-19 vaccines, help address the concerns of vaccine sceptics, and help develop more effective policies and communication strategies to maximize uptake.

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Section: Methodsmentioning

confidence: 99%

Artificial Intelligence–Enabled Analysis of Public Attitudes on Facebook and Twitter Toward COVID-19 Vaccines in the United Kingdom and the United States: Observational Study

Hussain¹,

Tahir²,

Hussain³

et al. 2021

J Med Internet Res

244

204

View full text Add to dashboard Cite

show abstract

“…removing links, hashtags, stop words) and a new hierarchical hybrid-ensemble based AI model was developed for thematic sentiment analysis. This utilised an average-weighting ensemble [20] of two lexicon-based methods: Valence-Aware Dictionary and sEntiment-Reasoner (VADER) [21] and TextBlob [22],. These were combined with a pre-trained DL-based model: Bidirectional Encoder-Representations from Transformers (BERT) [23], using a rule-based ensemble method, as illustrated in Figure 1.…”

Section: Methodsmentioning

confidence: 99%

Artificial intelligence-enabled analysis of UK and US public attitudes on Facebook and Twitter towards COVID-19 vaccinations

Hussain

Tahir

Hussain

et al. 2020

Preprint

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BackgroundGlobal efforts towards the development and deployment of a vaccine for SARS-CoV-2 are rapidly advancing. We developed and applied an artificial-intelligence (AI)-based approach to analyse social-media public sentiment in the UK and the US towards COVID-19 vaccinations, to understand public attitude and identify topics of concern.MethodsOver 300,000 social-media posts related to COVID-19 vaccinations were extracted, including 23,571 Facebook-posts from the UK and 144,864 from the US, along with 40,268 tweets from the UK and 98,385 from the US respectively, from 1st March - 22nd November 2020. We used natural language processing and deep learning based techniques to predict average sentiments, sentiment trends and topics of discussion. These were analysed longitudinally and geo-spatially, and a manual reading of randomly selected posts around points of interest helped identify underlying themes and validated insights from the analysis.ResultsWe found overall averaged positive, negative and neutral sentiment in the UK to be 58%, 22% and 17%, compared to 56%, 24% and 18% in the US, respectively. Public optimism over vaccine development, effectiveness and trials as well as concerns over safety, economic viability and corporation control were identified. We compared our findings to national surveys in both countries and found them to correlate broadly.ConclusionsAI-enabled social-media analysis should be considered for adoption by institutions and governments, alongside surveys and other conventional methods of assessing public attitude. This could enable real-time assessment, at scale, of public confidence and trust in COVID-19 vaccinations, help address concerns of vaccine-sceptics and develop more effective policies and communication strategies to maximise uptake.

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“…R s was recently used as a surrogate variable to reduce the uncertainty of ET o projection data [91]. This can be justified by the findings from Figure 5a, in which R s displayed a more profound impact on ET o than the other forcing variables.…”

Section: How Shapley Analysis Results Compare To Findings In the Currmentioning

confidence: 99%

Reliable Evapotranspiration Predictions with a Probabilistic Machine Learning Framework

Başağaoğlu¹,

Chakraborty

Winterle³

2021

Water

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Evapotranspiration is often expressed in terms of reference crop evapotranspiration (ETo), actual evapotranspiration (ETa), or surface water evaporation (Esw), and their reliable predictions are critical for groundwater, irrigation, and aquatic ecosystem management in semi-arid regions. We demonstrated that a newly developed probabilistic machine learning (ML) model, using a hybridized “boosting” framework, can simultaneously predict the daily ETo, Esw, & ETa from local hydroclimate data with high accuracy. The probabilistic approach exhibited great potential to overcome data uncertainties, in which 100% of the ETo, 89.9% of the Esw, and 93% of the ETa test data at three watersheds were within the models’ 95% prediction intervals. The modeling results revealed that the hybrid boosting framework can be used as a reliable computational tool to predict ETo while bypassing net solar radiation calculations, estimate Esw while overcoming uncertainties associated with pan evaporation & pan coefficients, and predict ETa while offsetting high capital & operational costs of EC towers. In addition, using the Shapley analysis built on a coalition game theory, we identified the order of importance and interactions between the hydroclimatic variables to enhance the models’ transparency and trustworthiness.

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A surrogate weighted mean ensemble method to reduce the uncertainty at a regional scale for the calculation of potential evapotranspiration

Abstract: This suggests that the SWME method would provide an opportunity to reduce the uncertainty in the projection of an ecological variable, which merits further application to diverse climate change projection studies in natural and agricultural ecosystems.

Cited by 16 publications

References 38 publications

Artificial Intelligence–Enabled Analysis of Public Attitudes on Facebook and Twitter Toward COVID-19 Vaccines in the United Kingdom and the United States: Observational Study

Artificial Intelligence–Enabled Analysis of Public Attitudes on Facebook and Twitter Toward COVID-19 Vaccines in the United Kingdom and the United States: Observational Study

Artificial intelligence-enabled analysis of UK and US public attitudes on Facebook and Twitter towards COVID-19 vaccinations

Reliable Evapotranspiration Predictions with a Probabilistic Machine Learning Framework

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