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Integrated urban hydrometeorological, climate and environmental services: Concept, methodology and key messages
2020
Urban Climate
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This involves combining (dense) heterogeneous observation networks, high-resolution forecasts, multi-hazard early warning systems and climate services to assist cities in setting and implementing mitigation ...
producing and providing these services to respond to the hazards across a range of time scales (weather to climate). ...
Integration has proven an effective practice in multi-hazard early warning systems and provides a holistic approach to enhance resilience. ...
doi:10.1016/j.uclim.2020.100623
pmid:32292692
pmcid:PMC7128437
fatcat:2b5djubdgbgg5prybatec4fdeu
Predicting demand for air taxi urban aviation services using machine learning algorithms
2021
Journal of Air Transport Management
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2021 pre-print arXiv:2103.14604v1 fatcat:iwtjs2iqvnf7tmqve55ye6w52m
This research focuses on predicting the demand for air taxi urban air mobility (UAM) services during different times of the day in various geographic regions of New York City using machine learning algorithms ...
Several ride-related factors (such as month of the year, day of the week and time of the day) and weather-related variables (such as temperature, weather conditions and visibility) are used as predictors ...
Demand Prediction for Air Taxi Services As the concept of air taxi services is still in its early stages, research on forecasting the demand for such services is limited. ...
doi:10.1016/j.jairtraman.2021.102043
fatcat:uhyooywr55czhl2k7iidjsscvi
Air Quality Prediction in Smart Cities Using Machine Learning Technologies based on Sensor Data: A Review
2020
Applied Sciences
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The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. ...
This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. ...
Early Air Pollution Forecasting as a Service: an Ensemble Learning Approach [56] : is focused on the air pollution prediction using Multi-channel Ensemble Learning via Supervised Assignment (MELSA) algorithm ...
doi:10.3390/app10072401
fatcat:b3weeysblfhgdazm6adej253nm
An investigation into machine learning approaches for forecasting spatio-temporal demand in ride-hailing service
[article]
2017
arXiv
pre-print
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In this paper, we present machine learning approaches for characterizing and forecasting the short-term demand for on-demand ride-hailing services. ...
To better assess the quality of the models, they have been tested on a real case study using the data of DiDi Chuxing, the main on-demand ride hailing service provider in China. ...
Similar to the decision trees-based algorithms, our ANN-based approach is also an ensemble learning technique. ...
arXiv:1703.02433v1
fatcat:43s3q7i7pnga7nwtwnw6o3hjo4
Machine learning approaches to predict peak demand days of cardiovascular admissions considering environmental exposure
2020
BMC Medical Informatics and Decision Making
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However, few studies have attempted to adopt machine learning approaches with excellent predictive abilities to forecast the healthcare demand for CVDs. ...
Accumulating evidence has linked environmental exposure, such as ambient air pollution and meteorological factors, to the development and severity of cardiovascular diseases (CVDs), resulting in increased ...
Nevertheless, only a very limited number of studies have attempted to adopt machinelearning based data-driven approaches to forecast the demand for healthcare services associated with environmental exposure ...
doi:10.1186/s12911-020-1101-8
pmid:32357880
fatcat:qet2fnamtnf3ddsb5gvimmfgpe
Smart Climate Hydropower Tool: A Machine-Learning Seasonal Forecasting Climate Service to Support Cost–Benefit Analysis of Reservoir Management
2020
Atmosphere
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This study proposes a climate service named Smart Climate Hydropower Tool (SCHT) and designed as a hybrid forecast system for supporting decision-making in a context of hydropower production. ...
Final results are presented to the users through a user-friendly web interface developed from a tied connection with end-users in an effective co-design process. ...
The developed NARX model runs in a multi-core configuration and provides an ensemble of trained models as a result, thus being suitable for probabilistic analysis. ...
doi:10.3390/atmos11121305
fatcat:krazxewqovafrlwbsu37edjh5m
Forecasting Teleconsultation Demand Using an Ensemble CNN Attention-Based BILSTM Model with Additional Variables
2021
Healthcare
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To enhance the forecasting accuracy of daily teleconsultation demand, this study proposes an ensemble hybrid deep learning model. ...
Overall, the proposed ECA-BILSTM model with effective additional variables is a feasible promising approach in teleconsultation demand forecasting. ...
The Proposed Ensemble Hybrid Deep Learning Approach In this section, a novel ensemble hybrid deep learning approach named ECA-BILSTM is formulated for teleconsultation demand forecasts. ...
doi:10.3390/healthcare9080992
fatcat:hvxpqobrmjh77dh46wwzbxtjhi
Real-time air quality forecasting, part II: State of the science, current research needs, and future prospects
2012
Atmospheric Environment
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forecasting approaches to quantify the uncertainties of the forecasts. ...
These include bias adjustment techniques to correct biases in forecast products, chemical data assimilation techniques for improving chemical initial and boundary conditions as well as emissions, and ensemble ...
A near-perfect agreement was obtained using ensemble forecast. As of 2010, the ensemble approach coupled with data assimilation is employed to produce the ensemble forecast. ...
doi:10.1016/j.atmosenv.2012.02.041
fatcat:tqbzq64l25erxo2uqcbzfbjlkq
A Stacking Ensemble Model to Predict Daily Number of Hospital Admissions for Cardiovascular Diseases
2020
IEEE Access
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In this study, we proposed a stacking ensemble model with direct prediction strategy to predict the daily number of CVDs admissions using HAs data, air pollution data, and meteorological data. ...
INDEX TERMS Cardiovascular diseases, hospital admissions, machine learning, stacking ensemble model, sequential forward floating selection, direct prediction strategy. ...
CONCLUSION In this study, a stacking ensemble model was presented to forecast seven-days ahead HAs for CVDs using HAs data, meteorological data, and air pollution data. ...
doi:10.1109/access.2020.3012143
fatcat:4hzvlamzdvbhnotnmpvp3n5ykm
NI4OS-Europe Service evaluation by user communities
[article]
2021
Zenodo
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Thematic services belonging to the Life Science, Climate Science, Digital Cultural Heritage, as well as Computational Physics communities, together with generic and repository services were used as use ...
The researchers provided brief reports detailing their experience using these services, with an emphasis on the ease of access and usage. ...
Individual Use Cases Air-pollution prediction is a web-application that enables both publicly available datasets as well as services for execution of air pollution predictions. ...
doi:10.5281/zenodo.4964928
fatcat:eelgz3425zfw5lumyx76qxhzui
The UK-China Climate Science to Service Partnership
2021
Bulletin of The American Meteorological Society - (BAMS)
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AbstractWe present results from the first 6 years of this major UK government funded project to accelerate and enhance collaborative research and development in climate science, forge a strong strategic ...
important legacy for future collaboration in climate science and services. ...
Other outstanding issues are
428 now also being brought into scope, such as air pollution in major Chinese cities (e.g. ...
doi:10.1175/bams-d-20-0055.1
fatcat:hb53ap444vae7ku5covwmcyjtq
A Layered Recurrent Neural Network for Imputing Air Pollutants Missing Data and Prediction of NO 2, O 3, PM 10, and PM 2.5
[chapter]
2020
Forecasting in Mathematics - Recent Advances, New Perspectives and Applications [Working Title]
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Therefore, we suggested the use of a Layered Recurrent Neural Network (L-RNN) to impute the missing value(s) of air pollutant attributes then build forecasting models. ...
Air pollution is considered one of the primary problems that could cause many human health problems such as asthma, damage to lungs, and even death. ...
Acknowledgements The authors would like to acknowledgement Croatian Meteorological and Hydrological Service for their support. ...
doi:10.5772/intechopen.93678
fatcat:xcjdsbrsyjbbnngxkjbqng6cn4
Calibrating the CAMS European multi-model air quality forecasts for regional air pollution monitoring
[article]
2022
arXiv
pre-print
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We expect positive impacts of our research for identifying and set up reliable and economic air pollution early warning systems. ...
As a result of our analysis, the key role of pollutant real-time observations to be ingested in the calibration strategy clearly emerge especially in the shorter look-ahead forecast hours. ...
It is in this framework that the Air Quality thematic area of CAMS, the Copernicus Atmosphere Monitoring Service, monitors and forecasts European air quality long-range transport of pollutants (Marécal ...
arXiv:2201.13355v1
fatcat:m3hjfhc67fdlzlhvu3s73xvjhe
Deep Air Quality Forecasts: Suspended Particulate Matter Modelling with Convolutional Neural and Long Short-Term Memory Networks
2020
IEEE Access
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CONCLUSION Using deep learning approach, this study reports the potential utility of an air pollution forecasting system developed and evaluated at hourly timesteps. ...
Also, considering the chaotic nature of air pollutants, the use of an improved complete ensemble empirical mode decomposition with an adaptive noise algorithm to extract temporal information of air pollutant ...
doi:10.1109/access.2020.3039002
fatcat:o5pbw4kyxzf6phipkmqy7oghk4
Semi-supervised Hybrid Modeling of Atmospheric Pollution in Urban Centers
[chapter]
2016
Communications in Computer and Information Science
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In this paper we propose a novel and flexible hybrid machine learning system that combines Semi-Supervised Classification and Semi-Supervised Clustering, in order to realize prediction of air pollutants ...
Air pollution is directly linked with the development of technology and science, the progress of which besides significant benefits to mankind it also has adverse effects on the environment and hence on ...
This method was based on the development of 117 partial ANN whose performance was averaged by using an ensemble learning approach. ...
doi:10.1007/978-3-319-44188-7_4
fatcat:3uqr7c5zb5h2hgrofelt7z6cfm
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