OOIR: Observatory of International Research

Papers

(The H4-Index of Computers & Geosciences is 34. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2020-05-01 to 2024-05-01.)

Article	Citations
Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping	187
Comparative study of landslide susceptibility mapping with different recurrent neural networks	169
Evaluation of machine learning methods for lithology classification using geophysical data	98
A review of Earth Artificial Intelligence	81
“sen2r”: An R toolbox for automatically downloading and preprocessing Sentinel-2 satellite data	77
PINNeik: Eikonal solution using physics-informed neural networks	69
Strip pooling channel spatial attention network for the segmentation of cloud and cloud shadow	67
Recognizing multivariate geochemical anomalies for mineral exploration by combining deep learning and one-class support vector machine	64
Deep learning-based method for SEM image segmentation in mineral characterization, an example from Duvernay Shale samples in Western Canada Sedimentary Basin	63
A comparative machine learning study for time series oil production forecasting: ARIMA, LSTM, and Prophet	62
A new strategy for spatial predictive mapping of mineral prospectivity: Automated hyperparameter tuning of random forest approach	55
Combining a class-weighted algorithm and machine learning models in landslide susceptibility mapping: A case study of Wanzhou section of the Three Gorges Reservoir, China	52
Knowledge graph construction and application in geosciences: A review	52
Semantic segmentation of high-resolution remote sensing images based on a class feature attention mechanism fused with Deeplabv3+	51
Deep learning of rock images for intelligent lithology identification	48
Developing comprehensive geocomputation tools for landslide susceptibility mapping: LSM tool pack	47
A comparative study of machine learning and Fuzzy-AHP technique to groundwater potential mapping in the data-scarce region	46
Hybrid geological modeling: Combining machine learning and multiple-point statistics	46
Imputation of missing well log data by random forest and its uncertainty analysis	46
The effect of calibration data length on the performance of a conceptual hydrological model versus LSTM and GRU: A case study for six basins from the CAMELS dataset	46
Machine learning in ground motion prediction	45
Inversion of 1D frequency- and time-domain electromagnetic data with convolutional neural networks	40
A machine learning methodology for multivariate pore-pressure prediction	40
Deep generative models in inversion: The impact of the generator's nonlinearity and development of a new approach based on a variational autoencoder	40
A positive and unlabeled learning algorithm for mineral prospectivity mapping	39

3D geological structure inversion from Noddy-generated magnetic data using deep learning methods	38
Deep convolutional neural network for automatic fault recognition from 3D seismic datasets	38
A hybrid prediction model of landslide displacement with risk-averse adaptation	37
Numerical modelling of deep coaxial borehole heat exchangers in the Cheshire Basin, UK	37
A stacking methodology of machine learning for 3D geological modeling with geological-geophysical datasets, Laochang Sn camp, Gejiu (China)	35
EMagPy: Open-source standalone software for processing, forward modeling and inversion of electromagnetic induction data	35
Spatiotemporal causal convolutional network for forecasting hourly PM2.5 concentrations in Beijing, China	35
A new structure for representing and tracking version information in a deep time knowledge graph	34
Flood monitoring by integration of Remote Sensing technique and Multi-Criteria Decision Making method	34
Aboveground biomass estimates of tropical mangrove forest using Sentinel-1 SAR coherence data - The superiority of deep learning over a semi-empirical model	34