Geomatics, Natural Hazards and Risk

Aims & Scope

The aim of Geomatics, Natural Hazards and Risk is to address new concepts, approaches and case studies using geospatial and remote sensing techniques to study monitoring, mapping, risk mitigation, risk vulnerability and early warning of natural hazards.

Geomatics, Natural Hazards and Risk covers the following topics: - Remote sensing techniques - Natural hazards associated with land, ocean, atmosphere, land-ocean-atmosphere coupling and climate change - Emerging problems related to multi-hazard risk assessment, multi-vulnerability risk assessment, risk quantification and the economic aspects of hazards. - Results of findings on major natural hazards

Metrics & Ranking

Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Directory of Open Access Journals (DOAJ)
• Web of Science

Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Earth and Planetary Sciences and Environmental Science, designed to support cutting-edge academic discovery.

Licensing & Copyright

This journal operates under an Open Access model. Articles are freely accessible to the public immediately upon publication. The content is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing users to share and adapt the work with proper attribution.

Copyright remains with the author(s), and no permission is required for non-commercial use, provided the original source is cited.

Policy Links

This section provides access to essential policy documents, guidelines, and resources related to the journal’s publication and submission processes.

• Aims scope
• Homepage
• Oa statement
• Author instructions
• License terms
• Review url
• Board url
• Copyright url
• Preservation url
• Apc url
• License

APC Details

The journal’s Article Processing Charge (APC) policies support open access publishing in Earth and Planetary Sciences and Environmental Science, ensuring accessibility and quality in research dissemination.

This journal requires an Article Processing Charge (APC) to support open access publishing, covering peer review, editing, and distribution. The current APC is 2,195.00 USD. Learn more.

Explore journals without APCs for alternative publishing options.

Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN)

  Citation: 441

  Authors: Bahareh, Biswajeet, Seyed Amir, Alireza, Shattri

• Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS

  Citation: 367

  Authors: Hossein, Biswajeet, Haleh, Noordin, Abdul Halim bin

• Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis

  Citation: 351

  Authors: Omid, Hossein, Mosa

• Multipurpose UAV for search and rescue operations in mountain avalanche events

  Citation: 323

  Authors: Mario, Andrea, Enrico, Marcello

• Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea

  Citation: 313

  Authors: Sunmin, Jeong-Cheol, Hyung-Sup, Moung Jin, Saro

• GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques

  Citation: 240

  Authors: Mahyat, Farzin, Mustafa, Haoyuan, Wei, Xiaoshen

• Weights-of-evidence model applied to landslide susceptibility mapping in a tropical hilly area

  Citation: 207

  Authors: Biswajeet, Hyun-Joo, Manfred

• GIS-based landslide susceptibility modelling: a comparative assessment of kernel logistic regression, Naïve-Bayes tree, and alternating decision tree models

  Citation: 201

  Authors: Wei, Xiaoshen, Jianbing, Jiale, Zhao, Haoyuan

• A comparative assessment of prediction capabilities of Dempster–Shafer and Weights-of-evidence models in landslide susceptibility mapping using GIS

  Citation: 185

  Authors: HamidReza, Biswajeet, Candan, Kimia Deylami

• A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China

  Citation: 183

  Authors: Wei, Ataollah, Himan, Baharin Bin, Shuai, Haoyuan, Ning