Natural Hazards and Environmental Challenges in the Anthropocene Age, 2nd Edition

Topic Information

Dear Colleagues,

The incidence of hazardous events has increased in recent years. While some hazardous events are triggered by natural processes, others are induced by human-related activities such as urbanization, industrialization, construction, negligence, etc. In this Topic, the role/contribution of humans in the occurrence of hazards and the impacts of these incidents on humanity and the environment are examined. Covering both natural and human-induced hazards, this Topic aims to publish a collection of innovative original research papers, case studies, and review papers addressing a wide range of concerns regarding natural hazards and the challenges they pose to man and the environment. We welcome multidisciplinary submissions on erosion, landslides, flooding, extreme temperatures, droughts, wildfire, tsunamis, volcanic eruptions, earthquakes, coastal hazards, subsidence, sinkholes, windstorms, tornadoes, etc. The incorporation of geospatial, (geo)statistical, numerical or index-based, and soft computational modelling techniques is highly encouraged. The themes that will be considered in this topic include, but are not limited to, the following:

• The modelling, prediction, characterization, and risk assessment of natural hazards.
• Anthropogenic and technological hazards in the present age: insights on the role of man.
• The impacts of natural hazard occurrences on man and the environment.
• Emerging technologies and sensors for geohazard data processing and interpretation.
• Climate change and the occurrence of mass movement and hydrometeorological hazards.
• Engineering failures related to water: their monitoring, assessment, and risk reduction.
• Extreme temperatures and environmental sustainability.
• The multi-hazard analysis and multiscale modelling of natural hazards.
• Seismic-related hazards and human settlement/displacement challenges.
• Age and gender exposure, response, and resilience to natural hazards.
• Social impacts of natural hazards in urban, semi-urban, and rural regions.
• Earth’s resources and hazards: the interactions of the Earth’s systems in hazardous events.
• Land use/land conservation strategies in the face of natural hazards.
• Planning, policymaking, and management strategies for hazard mitigation.
• The deterministic, computational, and stochastic modelling of hazard impacts.

The participating journals are Water, Geosciences, Geohazards, Environments and Atmosphere. We are delighted to invite you to submit your high-quality manuscript(s) to our Topic.

Dr. Quoc Bao Pham
Dr. Eyyüp Ensar Başakın
Prof. Dr. Hone-Jay Chu
Dr. Ömer Ekmekcioglu
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Atmosphere atmosphere	2.5	4.6	2010	16.1 Days	CHF 2400	Submit
GeoHazards geohazards	-	2.6	2020	19 Days	CHF 1000	Submit
Geosciences geosciences	2.4	5.3	2011	23.5 Days	CHF 1800	Submit
Water water	3.0	5.8	2009	17.5 Days	CHF 2600	Submit
Environments environments	3.5	5.7	2014	22.8 Days	CHF 1800	Submit

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All Atmosphere GeoHazards Geosciences Water Environments

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25 pages, 3496 KiB  

Open AccessArticle

Overviewing the Machine Learning Utilization on Groundwater Research Using Bibliometric Analysis

by Kayhan Bayhan, Eyyup Ensar Başakın, Ömer Ekmekcioğlu, Quoc Bao Pham and Hone-Jay Chu

Water 2025, 17(7), 936; https://doi.org/10.3390/w17070936 - 23 Mar 2025

Viewed by 622

Abstract

Groundwater, which constitutes 95% of the world’s freshwater resources, is widely used for drinking and domestic water supply, agricultural irrigation, energy production, bottled water production, and commercial use. In recent years, due to pressures from climate change and excessive urbanization, a noticeable decline [...] Read more.

Groundwater, which constitutes 95% of the world’s freshwater resources, is widely used for drinking and domestic water supply, agricultural irrigation, energy production, bottled water production, and commercial use. In recent years, due to pressures from climate change and excessive urbanization, a noticeable decline in groundwater levels has been observed, particularly in arid and semi-arid regions. The corresponding changes have been analyzed using a diverse range of methodologies, including data-driven modeling techniques. Recent evidence has shown a notable acceleration in the utilization of such advanced techniques, demonstrating significant attention by the research community. Therefore, the major aim of the present study is to conduct a bibliometric analysis to investigate the application and evolution of machine learning (ML) techniques in groundwater research. In this sense, studies published between 2000 and 2023 were examined in terms of scientific productivity, collaboration networks, research themes, and methods. The findings revealed that ML techniques offer high accuracy and predictive capacity, especially in water quality, water level estimation, and pollution modeling. The United States, China, and Iran stand out as leading countries emphasizing the strategic importance of ML in groundwater management. However, the outcomes demonstrated that a low level of international cooperation has led to deficiencies in solving transboundary water problems. The study aimed to encourage more widespread and effective use of ML techniques in water management and environmental planning processes and drew attention to the importance of transparent and interpretable algorithms, with the potential to yield rewarding opportunities in increasing the adoption of corresponding technologies by decision-makers. Full article

(This article belongs to the Topic Natural Hazards and Environmental Challenges in the Anthropocene Age, 2nd Edition)

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