The role of artificial intelligence and IoT in prediction of earthquakes: Review
December 2024
Earthquakes are classified as one of the most devastating natural disasters that can have catastrophic effects on the environment, lives, and properties. There has been an increasing interest in the...
Cellular automata models for simulation and prediction of urban land use change: Development and prospects
December 2025
Rapid urbanization and land-use changes are placing immense pressure on resources, infrastructure, and environmental sustainability. To address these, accurate urban simulation models are essential...
Vision-language models for automated carbonate petrography and depositional environment interpretation
September 2026
Carbonate petrographic analysis provides essential qualitative and semi-quantitative constraints on depositional environments and diagenetic evolution at microscale. However, conventional thin-section...
GIS-based wildfire prediction model in Indonesia using stacking ensemble learning
June 2026
In Indonesia, wildfires have become an annual disaster that results in significant losses across various aspects of life, including ecological, social, and economic conditions. To minimize these losses,...
Determination of future land use changes using remote sensing imagery and artificial neural network algorithm: A case study of Davao City, Philippines
December 2023
Land use and land cover (LULC) changes refer to alterations in land use or physical characteristics. These changes can be caused by human activities, such as urbanization, agriculture, and resource...
Scalable variational Gaussian process framework for implicit geological modelling and compositional grade interpolation
June 2026
Geological modelling and estimation of polymetallic ore grades require methods that simultaneously honour spatial heterogeneity, compositional constraints, and predictive uncertainty. We present a scalable...
Optimizing zero-shot text-based segmentation of remote sensing imagery using SAM and Grounding DINO
June 2025
The use of AI technologies in remote sensing (RS) tasks has been the focus of many individuals in both the professional and academic domains. Having more accessible interfaces and tools that allow people...
Simultaneous estimation of hyperparameters and basement depth in gravity data inversion using the JAYA algorithm
September 2026
Basement relief depth estimation is an important component in characterizing sedimentary basins, which serve as essential reservoir for geothermal energy, groundwater, and hydrocarbons. While residual...
Reservoir evaluation using petrophysics informed machine learning: A case study
December 2024
We propose a novel machine learning approach to improve the formation evaluation from logs by integrating petrophysical information with neural networks using a loss function. The petrophysical information...
A hybrid Prophet–LSTM–BPNN framework for seasonal drought prediction
September 2026
Drought is among the most destructive hydroclimatic hazards, particularly in arid and semi-arid regions, where water scarcity directly threatens agricultural production and socioeconomic stability....
An open benchmark dataset of synthetic seismic data and real swell noise for evaluating deep learning denoising models
June 2026
Recent advances in deep learning (DL) have been fostered by open benchmark datasets that allow reproducible and systematic evaluation of models. Despite the increasing adoption of DL methods in geophysics,...
Leveraging boosting machine learning for drilling rate of penetration (ROP) prediction based on drilling and petrophysical parameters
June 2025
Drilling optimization requires accurate drill bit rate-of-penetration (ROP) predictions. ROP decreases drilling time and costs and increases rig productivity. This study employs random forest (RF),...
Explainable AI for microseismic event detection
September 2026
Deep neural networks like PhaseNet show high accuracy in detecting microseismic events, but their black-box nature is a concern in critical applications. We apply Explainable Artificial Intelligence...
Deep learning based identification of rock minerals from un-processed digital microscopic images of undisturbed broken-surfaces
June 2025
This study employed convolutional neural networks (CNNs) for the classification of rock minerals based on 3179 RGB-scale original microstructural images of undisturbed broken surfaces. The image dataset...
AI-based approaches for wetland mapping and classification: A review of current practices and future perspectives
December 2025
Wetlands are critical ecosystems that provide essential ecological, hydrological, and socio-economic services, such as water purification, climate regulation, and biodiversity conservation. However,...
Stability prediction of footings on slopes with dense sand using Bolton model, FELA, XGBoost, Random Forest, and Evolutionary Polynomial Regression
September 2026
This study investigates the bearing-capacity factor (Nγ) of rigid strip footings placed on dense sand slopes by integrating finite element limit analysis (FELA) with machine learning and a symbolic...
Deep learning-based downscaling of ERA5-Land temperature to 250 m resolution over the Trentino–South Tyrol Alpine region
June 2026
High-resolution near-surface temperature data are essential in mountainous regions, where complex topography induces strong spatial and temporal variability. However, coarse-resolution reanalysis products...
Benchmarking data handling strategies for landslide susceptibility modeling using random forest workflows
December 2024
Machine learning (ML) algorithms are frequently used in landslide susceptibility modeling. Different data handling strategies may generate variations in landslide susceptibility modeling, even when...
LatentPINNs: Generative physics-informed neural networks via a latent representation learning
June 2025
Physics-informed neural networks (PINNs) are promising to replace conventional mesh-based partial differential equation (PDE) solvers by offering more accurate and flexible PDE solutions. However, PINNs...
HCEC: An effective hybrid CNN-ensemble classifier for hyperspectral image classification
September 2026
Hyperspectral image classification (HSI) is extensively utilized to analyze remotely sensed images for different real-world applications. Recently, convolutional neural network(CNN) have been applied...
Deep learning preconditioned methods for frequency-domain acoustic wave equation with their wavefield simulations
September 2026
To overcome the low efficiency of performing frequency-domain acoustic wavefield simulation, this paper proposes a kind of deep learning (DL) preconditioned methods based on sufficiently training deep...
Estimation of interval P-wave velocities from Dix slowness using implicit neural representation
September 2026
Mapping time-migration velocities to depth-domain interval velocities in the presence of lateral variation is important in seismic exploration. The first step of this process, computing Dix velocities...
Spatial mapping and modelling of soil organic carbon using random forest and remote sensing variables in part of Kaduna, Northern Nigeria
March 2026
Reliable and up-to-date digital soil data is crucial for achieving Sustainable Development Goal 13 (Climate Action) by enabling improved monitoring of soil carbon and land degradation, thereby supporting...
SeisReconNO: Leveraging a U-Net-Enhanced Fourier neural operator for 3D seismic reconstruction
June 2026
Missing traces in 3D seismic data are a recurring challenge caused by receiver malfunctions, acquisition limitations, and geological or environmental constraints. These gaps hinder accurate interpretation...
A hybrid ensemble deep learning model for advanced time series rainfall forecasting using satellite data and climate variability analysis
June 2026
Accurate rainfall prediction is important for climate adaptation, managing water resources, and planning for farming in dry areas and places where data is difficult to obtain. By collecting long-term...
