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Integrated urban hydrometeorological, climate and environmental services: Concept, methodology and key messages

Sue Grimmond, Veronique Bouchet, Luisa T. Molina, Alexander Baklanov, Jianguo Tan, K. Heinke Schluenzen, Gerald Mills, Brian Golding, Valery Masson, Chao Ren, James Voogt, Shiguang Miao (+6 others)
2020 Urban Climate  
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

Suchithra Rajendran, Sharan Srinivas, Trenton Grimshaw
2021 Journal of Air Transport Management  
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

Ditsuhi Iskandaryan, Francisco Ramos, Sergio Trilles
2020 Applied Sciences  
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]

Ismaïl Saadi, Melvin Wong, Bilal Farooq, Jacques Teller, Mario Cools
2017 arXiv   pre-print
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

Hang Qiu, Lin Luo, Ziqi Su, Li Zhou, Liya Wang, Yucheng Chen
2020 BMC Medical Informatics and Decision Making  
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

Arthur H. Essenfelder, Francesca Larosa, Paolo Mazzoli, Stefano Bagli, Davide Broccoli, Valerio Luzzi, Jaroslav Mysiak, Paola Mercogliano, Francesco dalla Valle
2020 Atmosphere  
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

Wenjia Chen, Jinlin Li
2021 Healthcare  
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

Yang Zhang, Marc Bocquet, Vivien Mallet, Christian Seigneur, Alexander Baklanov
2012 Atmospheric Environment  
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

Zhixu Hu, Hang Qiu, Ziqi Su, Minghui Shen, Ziyu Chen
2020 IEEE Access  
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]

Chrysovalantis Constantinou, Alexis Chatzigoulas, Aspasia Vozi, Michail Papadourakis, Dimitris Papakonstantinou, Zoe Cournia, Ljupco Pejov, Anastas Mishev, Bojana Kotetska, Theodoros Christoudias, George Mikuchadze, Hrachya Astsatryan (+6 others)
2021 Zenodo  
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

Adam A. Scaife, Elizabeth Good, Ying Sun, Zhongwei Yan, Nick Dunstone, Hong-Li Ren, Chaofan Li, Riyu Lu, Peili Wu, Zongjian Ke, Zhuguo Ma, Kalli Furtado (+13 others)
2021 Bulletin of The American Meteorological Society - (BAMS)  
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]

Hamza Turabieh, Alaa Sheta, Malik Braik, Elvira Kovač-Andrić
2020 Forecasting in Mathematics - Recent Advances, New Perspectives and Applications [Working Title]  
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]

Gabriele Casciaro, Mattia Cavaiola, Andrea Mazzino
2022 arXiv   pre-print
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

Ekta Sharma, Ravinesh C. Deo, Ramendra Prasad, Alfio V. Parisi, Nawin Raj
2020 IEEE Access  
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]

Ilias Bougoudis, Konstantinos Demertzis, Lazaros Iliadis, Vardis-Dimitris Anezakis, Antonios Papaleonidas
2016 Communications in Computer and Information Science  
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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