How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions

AY Sun, BR Scanlon - Environmental Research Letters, 2019 - iopscience.iop.org
Big Data and machine learning (ML) technologies have the potential to impact many facets
of environment and water management (EWM). Big Data are information assets …

Social media data for environmental sustainability: A critical review of opportunities, threats, and ethical use

A Ghermandi, J Langemeyer, D Van Berkel, F Calcagni… - One Earth, 2023 - cell.com
Social media data are transforming sustainability science. However, challenges from
restrictions in data accessibility and ethical concerns regarding potential data misuse have …

Social media for intelligent public information and warning in disasters: An interdisciplinary review

C Zhang, C Fan, W Yao, X Hu, A Mostafavi - International Journal of …, 2019 - Elsevier
Social media offers participatory and collaborative structure and collective knowledge
building capacity to the public information and warning approaches. Therefore, the author …

Participatory sensing and digital twin city: Updating virtual city models for enhanced risk-informed decision-making

Y Ham, J Kim - Journal of Management in Engineering, 2020 - ascelibrary.org
The benefits of a digital twin city have been assessed based on real-time data collected from
preinstalled Internet of Things (IoT) sensors (eg, traffic, energy use, air pollution, water …

Management of climate resilience: exploring the potential of digital twin technology, 3D city modelling, and early warning systems

K Riaz, M McAfee, SS Gharbia - Sensors, 2023 - mdpi.com
Cities, and in particular those in coastal low-lying areas, are becoming increasingly
susceptible to climate change, the impact of which is worsened by the tendency for …

Citizens AND HYdrology (CANDHY): conceptualizing a transdisciplinary framework for citizen science addressing hydrological challenges

F Nardi, C Cudennec, T Abrate, C Allouch… - Hydrological …, 2022 - Taylor & Francis
Widely available digital technologies are empowering citizens who are increasingly well
informed and involved in numerous water, climate, and environmental challenges. Citizen …

Identifying disaster-related tweets and their semantic, spatial and temporal context using deep learning, natural language processing and spatial analysis: a case …

MA Sit, C Koylu, I Demir - Social Sensing and Big Data Computing …, 2020 - taylorfrancis.com
We introduce an analytical framework for analyzing tweets to (1) identify and categorize fine-
grained details about a disaster such as affected individuals, damaged infrastructure and …

Data deprivations, data gaps and digital divides: Lessons from the COVID-19 pandemic

W Naudé, R Vinuesa - Big Data & Society, 2021 - journals.sagepub.com
This paper draws lessons from the COVID-19 pandemic for the relationship between data-
driven decision making and global development. The lessons are that (i) users should keep …

A systematic review of existing early warning systems' challenges and opportunities in cloud computing early warning systems

IE Agbehadji, T Mabhaudhi, J Botai, M Masinde - Climate, 2023 - mdpi.com
This paper assessed existing EWS challenges and opportunities in cloud computing through
the PSALSAR framework for systematic literature review and meta-analysis. The research …

Disaster damage assessment based on fine-grained topics in social media

M Dou, Y Wang, Y Gu, S Dong, M Qiao… - Computers & Geosciences, 2021 - Elsevier
Social media data have been widely used to enrich human-centric information for situational
awareness and disaster assessment. Owing to the granularity of topics detected from …