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Feature Papers for AI and Big Data in Earth Science
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Feature Papers for AI and Big Data in Earth Science

A special issue of Earth (ISSN 2673-4834). This special issue belongs to the section "AI and Big Data in Earth Science".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 3021

Special Issue Editor

Dr. Hossein Bonakdari

E-Mail Website
Guest Editor
Department of Civil Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada
Interests: water resources management; hydrological modeling; artificial intelligence; sustainable development; time series
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancement of artificial intelligence (AI), machine learning (ML), and big data analytics is revolutionizing how we observe, model, and understand the Earth system. With the growing availability of high-resolution satellite data, sensor networks, and large-scale simulations, Earth science is becoming increasingly data-intensive. These emerging technologies enable the extraction of hidden patterns, improve forecasting accuracy, and enhance our ability to assess and mitigate environmental risks.

At the same time, the increasing availability of diverse remote sensing data, such as radar, optical, and LiDAR acquired through multiplatform (drone and satellite) and multi-resolution systems, is reshaping how Earth processes are observed, analyzed, and modeled. The integration of heterogeneous datasets with AI-driven methodologies offers new opportunities to develop more accurate, interpretable, and scalable models of environmental dynamics.

This Special Issue welcomes manuscripts addressing, but not limited to, the following themes:

  1. AI and machine learning applications in geoscience, climate, and environmental modeling.
  2. Big data analytics and data fusion for remote sensing and Earth observation.
  3. Integration of radar, optical, and LiDAR data from multiplatform and multi-resolution systems.
  4. Open-source and cloud-based frameworks for large-scale environmental data analysis.
  5. Interpretable AI, uncertainty quantification, and model validation in Earth system studies.

Dr. Hossein Bonakdari
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Earth is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • big data
  • remote sensing
  • radar
  • optical
  • LiDAR
  • multiplatform observation
  • multi-resolution analysis
  • earth system modeling

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  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

28 pages, 3802 KB  
Article
Modeling Flood Susceptibility in Rwanda Using an AI-Enabled Risk Mapping Tool
by Yves Hategekimana, Valentine Mukanyandwi, Georges Kwizera, Fidele Karamage, Emmanuel Ntawukuriryayo, Fabrice Manzi, Gaspard Rwanyiziri and Moise Busogi
Earth 2026, 7(2), 53; https://doi.org/10.3390/earth7020053 - 21 Mar 2026
Viewed by 892
Abstract
This study presents the development of a Python-based flood-susceptibility risk-mapping tool, implemented in Jupyter Notebook, applied to Rwanda. A Flood Susceptibility Index (FSI) was developed by integrating 20 causal factors associated with flood occurrences, including topographic, hydrological, geological, and anthropogenic variables. Logistic regression, [...] Read more.
This study presents the development of a Python-based flood-susceptibility risk-mapping tool, implemented in Jupyter Notebook, applied to Rwanda. A Flood Susceptibility Index (FSI) was developed by integrating 20 causal factors associated with flood occurrences, including topographic, hydrological, geological, and anthropogenic variables. Logistic regression, and Variance Inflation Factor were implemented in Python using libraries such as Numpy, Arcpy, traceback, scipy, Pandas, Seaborn, and statsmodel to assign weights to each factor, and to address multicollinearity. The model was validated against flood extent data derived from Sentinel-1 satellite imagery for the major historical flood event that occurred from 2014 to 2024, ensuring spatial consistency and predictive reliability. To project future flood susceptibility for 2030, precipitation data from the Institut Pierre Simon Laplace Coupled Model, version 5A, Medium Resolution (IPSL-CM5A-MR) climate model under the Representative Concentration Pathway 8.5 (RCP 8.5) scenario were utilized. The resulting FSI was classified into five susceptibility levels, from very low to very high, and visualized using Python’s geospatial and plotting tools within Jupyter Notebook in ArcGIS Pro 3.5. It indicates that areas with high amounts of rainfall, and proximity to wetlands and rivers reveal the highest flood risk. The automated and reproducible approach offered by Python enhances transparency and scalability, providing a decision-support tool for disaster risk reduction and climate adaptation planning in Rwanda. Full article
(This article belongs to the Special Issue Feature Papers for AI and Big Data in Earth Science)
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23 pages, 5440 KB  
Article
Risk Assessment of Land Subsidence Hazard Due to Groundwater Depletion for Water Conservation
by Ni Made Pertiwi Jaya and Masahiko Nagai
Earth 2026, 7(1), 29; https://doi.org/10.3390/earth7010029 - 15 Feb 2026
Viewed by 673
Abstract
Hazard risk monitoring of groundwater depletion and land subsidence due to excessive groundwater extraction is crucial for groundwater resource development, especially in densely populated, small-island developing sites. The island of Bali, Indonesia, represents such an urban environment at risk of land subsidence arising [...] Read more.
Hazard risk monitoring of groundwater depletion and land subsidence due to excessive groundwater extraction is crucial for groundwater resource development, especially in densely populated, small-island developing sites. The island of Bali, Indonesia, represents such an urban environment at risk of land subsidence arising from groundwater depletion. The total percentage of groundwater depletion was calculated and interpolated spatially using measurements of groundwater level from 2008 to 2017 at 18 monitoring well sites available in the area. Furthermore, time-series synthetic-aperture radar (SAR) interferometry processing was applied to estimate the temporal change in land displacement using the Phased Array type L-band SAR (PALSAR) data from 2007 to 2010. The result of downward displacement, signifying subsidence, corresponded with the Global Navigation Satellite System (GNSS) data measurements at stations distributed in the observed subsided areas, i.e., CDNP and CPBI. The displacement varied consistently with changes in groundwater level. In regard to maintaining groundwater utilization, the hazard–risk relation of the groundwater depletion, i.e., low (<10%), moderate (10–25%), and high (>25%), and the presence/absence of subsidence were utilized to classify groundwater conservation into safe, vulnerable, critical, and damaged zones. This application can be considered effective in providing spatial information for sustainable groundwater management. Full article
(This article belongs to the Special Issue Feature Papers for AI and Big Data in Earth Science)
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21 pages, 4938 KB  
Article
Impact of LULC Classification Methods on Runoff Simulation in an Arid Mountainous Watershed Using Remote Sensing and Machine Learning
by Ali Ibrahim, Ahmed Wageeh, Mohamed A. Hamouda, Alaa Ahmed and Ahmed Gad
Earth 2026, 7(1), 26; https://doi.org/10.3390/earth7010026 - 11 Feb 2026
Cited by 1 | Viewed by 950
Abstract
Reliable hydrologic modeling in arid, topographically complex watersheds depends on accurate land-use/land-cover (LULC) representation. This study evaluates how different LULC categorization methods affect simulated runoff for the Wadi Hatta watershed (UAE) using a GIS-driven machine learning framework that combines high-resolution remote sensing with [...] Read more.
Reliable hydrologic modeling in arid, topographically complex watersheds depends on accurate land-use/land-cover (LULC) representation. This study evaluates how different LULC categorization methods affect simulated runoff for the Wadi Hatta watershed (UAE) using a GIS-driven machine learning framework that combines high-resolution remote sensing with hydrologic modeling. LULC maps were generated in Google Earth Engine using Random Forest (RF) and Support Vector Machine (SVM) classifiers applied to Sentinel-2 (10 m) and Landsat 8/9 (30 m) imageries and compared with the 10 m ESRI predefined LULC dataset. The resulting LULC classifications were converted to SCS Curve Numbers and used in HEC-HMS hydrologic modeling to simulate runoff under a 50-year design storm, under consistent meteorological and physical conditions. Results show that Sentinel-2 + SVM achieved the highest classification accuracy (overall accuracy up to 0.86) and produced the earliest and highest simulated peak discharge (11.4 m3/s), reflecting improved detection of impervious surfaces. In contrast, the Landsat-9 + RF scenario yielded the lowest peak (7.5 m3/s), consistent with a higher proportion of pervious land covers. LULC change analysis between 2017 and 2024 showed increases in forest cover (1.0–3.3%) and built-up areas (6.0–7.9%) driven by afforestation and urban expansion. These results demonstrate that LULC input resolution and classifier selection significantly influence hydrologic model sensitivity and runoff estimates, underscoring the need for carefully selected, high-resolution LULC products in flood risk assessment and water resource planning in data-scarce arid environments. Full article
(This article belongs to the Special Issue Feature Papers for AI and Big Data in Earth Science)
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