Integrated gravity and landsat analyses for hydrothermal alteration and fault zone mapping in the lokop geothermal prospect, Indonesia - Faisal Abdullah; Muhammad Yanis; Muhammad Ridha Adhari; Dian Darisma; Gartika Satria Nugraha; Muzakir Zainal; Nazli Ismail (Earth Science Informatics, 2026)

Lithology-driven machine and deep learning modelling for porosity prediction in shale reservoir systems using well logging data - Shaukat Khan; Zhishui Liu; Zhiqiang Lu; Wakeel Hussain; Muhammad Sajid; Manaf Muhammad; Abdul Wahid; Muhammad Ubaid Umar (Earth Science Informatics, 2026)

Data augmentation and normalization strategies for improving satellite image segmentation - Ayesha Saadia; Muhammad Ishfaq; Muhammad Umair; Iqbal Murtza; Munahil Yaqoob (Earth Science Informatics, 2026)

Geospatial mapping of potential aggregate resources using integrated GIS-AHP, geotechnical, petrographic and machine learning approaches - Javid Hussain; Nafees Ali; Xiaodong Fu; Jian Chen; Sayed Muhammad Iqbal; Altaf Hussain; Hikmat Salam (Earth Science Informatics, 2025)

Recognition of rock formation while drilling based on optimized machine learning algorithms - Cancan Liu; Xigui Zheng; Haifeng Wang; Niaz Muhammad Shahani; Yankun Ma; Zike Meng (Earth Science Informatics, 2025)

Structural architecture of al-madinah, saudi arabia: an integrated analysis using remote sensing and aeromagnetic data - Eslam Elawadi; Sayed S. R. Moustafa; Mohamed Metwaly; Muhammad Waqar Azeem (Earth Science Informatics, 2025)

Predicting water quality variables using gradient boosting machine: global versus local explainability using SHapley Additive Explanations (SHAP) - Khaled Merabet; Fabio Di Nunno; Francesco Granata; Sungwon Kim; Rana Muhammad Adnan; Salim Heddam; Ozgur Kisi; Mohammad Zounemat-Kermani (Earth Science Informatics, 2025)

Morphometric analysis of debris flow hazard and risk assessment in the mountain terrains of northern Pakistan using remote sensing and field data - Nisar Ali Shah; Muhammad Shafique; Lewis A. Owen; Yaseen AI-Mulla; Yaseen Ullah (Earth Science Informatics, 2025)

Advancing forest fire prediction: A multi-layer stacking ensemble model approach - Fahad Shahzad; Kaleem Mehmood; Shoaib Ahmad Anees; Muhammad Adnan; Sultan Muhammad; Ijlal Haidar; Jamshid Ali; Khadim Hussain; Zhongke Feng; Waseem Razzaq Khan (Earth Science Informatics, 2025)

Geostatistical and multivariate analysis of phosphate evolution and its relationship with heavy metals in shallow groundwater in a Semi-Arid Basin - Saadu Umar Wali; Noraliani Binti Alias; Abdulqadir Abubakar Usman; Abdullahi Umar; Nasiru Muhammad; Ismail Usman Kaoje; Buhari Samaila; Basiru Aliyu; Habiba Muhammad Ladan; Mudassiru Atiku; Sa’ad Ibrahim (Earth Science Informatics, 2025)

Innovative drought monitoring: development and application of the multi-regional aggregated standardized drought index (MRASDI) - Asad Ellahi; Ibrahim A. Nafisah; Mohammed M. A. Almazah; Nafisa A. Abasheir; Ijaz Hussain; Muhammad Mubashar Dogar (Earth Science Informatics, 2025)

Evaluating evapotranspiration models for precise aridity mapping based on UNEP- aridity classification - Dauda Pius Awhari; Mohamad Hidayat Bin Jamal; Mohd Khairul Idlan Muhammad; Istifanus Akila Bardey; Shamsuddin Shahid (Earth Science Informatics, 2025)

Marine landscape mapping for ecologically relevant habitat suitability modelling - Bryan Yong; Khaira Ismail; Azizi Ali; Rozaimi Che Hasan; Muhammad Abdul Hakim Muhamad (Earth Science Informatics, 2025)

Utilizing contemporary machine learning techniques for determining soilcrete properties - Waleed Bin Inqiad; Muhammad Saud Khan; Zohaib Mehmood; Naseer Muhammad Khan; Muhammad Bilal; Mohammed Sazid; Saad S. Alarifi (Earth Science Informatics, 2025)
Soilcrete is an innovative construction material made by combining naturally occurring earth materials with cement. It can be effectively used in areas where other construction materials are not readily available due to financial or environmental reasons since soilcrete is made from readily available natural clay. It can also help to cut down the greenhouse gas emissions from the construction industry by encouraging the use of resources that are locally available. Thus, it is imperative to reliably predict different properties of soilcrete since the accurate determination of these properties is crucial for the widespread use of soilcrete materials. However, the laboratory determination of these properties is subjected to significant time and resource constraints. As a result, this research was undertaken to provide empirical prediction models for the density, shrinkage, and strain of soilcrete mixes using two machine learning algorithms: Gene Expression Programming (GEP) and Extreme Gradient Boosting (XGB). The analysis revealed that XGB-based predictions correlated more with real-life values than GEP having training $${\text{R}}^{2}=0.999$$ R 2 = 0.999 for both density and shrinkage prediction and $${\text{R}}^{2}=0.944$$ R 2 = 0.944 for strain prediction. Moreover, several explanatory analyses including individual conditional expectation (ICE) analysis and shapely analysis were done on the XGB model which showed that water-to-binder ratio, metakaolin content, and modulus of elasticity are some of the most important variables for forecasting soilcrete materials properties. Furthermore, an interactive graphical user interface (GUI) has been developed for effective utilization in civil engineering industry to forecast these properties of soilcrete materials.

Predictive modeling of reservoir geomechanical parameters through computational intelligence approach, integrating core and well logging data - Sayed Muhammad Iqbal; Jianmin Li; Junxiu Ma; Dawei Hu; Shuang Tian; Hui Zhou; Litao Shang (Earth Science Informatics, 2025)

Optimizing support vector machines for enhanced permeability prediction in sandstone reservoirs using systematic and heuristic hyperparameter tuning - Jar Ullah; Huan Li; Rifaat M. Abdalla; Muhammad Asad; Amos P. K. Tai; Asim Falaknaz (Earth Science Informatics, 2025)

FloPy for optimizing the structure of hydraulic-driven groundwater circulation wells - Zhang Fang; Zhiguo Liu; Guowen Wang; Muhammad Sadam (Earth Science Informatics, 2025)

Comparative performance of four machine learning models for land cover classification in a low-cost UAV ultra-high-resolution RGB-only orthomosaic - Muhammad Abdullah Sohl; Syed Amer Mahmood; Mamoon Ur Rasheed (Earth Science Informatics, 2024)

Evaluating climate change for the early 21st century in the Potwar Region of Pakistan using CMIP6 simulations - Hania Batool; Muhammad Taqui (Earth Science Informatics, 2024)

Reservoir porosity assessment and anomaly identification from seismic attributes using Gaussian process machine learning - Maulana Hutama Rahma Putra; Maman Hermana; Ida Bagus Suananda Yogi; Touhid Mohammad Hossain; Muhammad Faris Abdurrachman; Said Jadid A. Kadir (Earth Science Informatics, 2024)
Porosity, as one of the reservoir properties, is an important parameter to numerous studies, i.e., the reservoir’s oil/gas volume estimation or even the storage capacity measurement in the Carbon Capture Storage (CCS) project. However, an approach to estimate porosity using elastic property from the inversion propagates its error, affecting the result’s accuracy. On the other hand, direct estimation from seismic data is another approach to estimating porosity, but it poses a high non-linear problem. Thus, we propose the non-parametric machine learning approach, Gaussian Process (GP), which draws distribution over the function to solve the high non-linear problem between seismic data with porosity and quantify the prediction uncertainty simultaneously. With the help of Random Forest (RF) as the feature selection method, the GP predictions show excellent results in the blind test, a well that is completely removed from the training data, and comparison with other machine learning models. The uncertainty, standard deviation from GP prediction, can act as a quantitative evaluation of the prediction result. Moreover, we generate a new attribute based on the quartile of the standard deviation to delineate the anomaly zones. High anomaly zones are highlighted and associated with high porosity from GP and low inverted P-impedance from inversion results. Thus, applying the GP using seismic data shows its potential to characterize the reservoir property spatially, and the uncertainty offers insights into quantitative and qualitative evaluation for hydrocarbon exploration and development.

An autoencoder-based stacked LSTM transfer learning model for EC forecasting - Abdullahi Uwaisu Muhammad; Hamza Djigal; Tasiu Muazu; Jibril Muhammad Adam; Abdoul Fatakhou Ba; Umar Sani Dabai; Sani Tijjani; Muhammad Sabo Yahaya; Aliyu Ashiru; Umar Muhammad Mustapha Kumshe; Saddam Aliyu; Faruwa Ajibola Richard (Earth Science Informatics, 2023)

Transfer learning for streamflow forecasting using unguaged MOPEX basins data set - Abdullahi Uwaisu Muhammad; S. I. Abba (Earth Science Informatics, 2023)

Estimation of glacier mass balance using remote sensing and GIS technology in the Hindu Kush region of northern Pakistan - Sidra Bibi; Muhammad Shafique; Neelum Ali; Shahla Nazneen; Liaqat Ali; Siddique Ullah Baig; Rehman Gul (Earth Science Informatics, 2023)

Spatio-temporal variations in characteristics of terrestrial water storage and associated drought over different geographic regions of Türkiye - Behnam Khorrami; Shoaib Ali; Leila Hossein Abadi; Muhammad Jehanzaib (Earth Science Informatics, 2023)

Oil palm yield prediction across blocks from multi-source data using machine learning and deep learning - Yuhao Ang; Helmi Zulhaidi Mohd Shafri; Yang Ping Lee; Shahrul Azman Bakar; Haryati Abidin; Mohd Umar Ubaydah Mohd Junaidi; Shaiful Jahari Hashim; Nik Norasma Che’Ya; Mohd Roshdi Hassan; Hwee San Lim; Rosni Abdullah; Yusri Yusup; et al. (Earth Science Informatics, 2022)

A robust autonomous navigation and mapping system based on GPS and LiDAR data for unconstraint environment - Jignesh Patoliya; Hiren Mewada; M. Hassaballah; Muhammad Attique Khan; Seifedine Kadry (Earth Science Informatics, 2022)

Enhanced statistical tests under indeterminacy with application to earth speed data - Muhammad Aslam (Earth Science Informatics, 2021)

Comparing different space-borne sensors and methods for the retrieval of land surface temperature - Arif UR Rehman; Sami Ullah; Qijing Liu; Muhammad Sadiq Khan (Earth Science Informatics, 2021)

Shadow detection of the satellite images of earth using ratio image pixels - Suhaib Musleh; Muhammad Sarfraz; Hazem Raafat (Earth Science Informatics, 2021)

Modelling of seismicity in southern pakistan using GIS techniques - Muhammad Jahangir Khan (Earth Science Informatics, 2020)

Predicting streamflows to a multipurpose reservoir using artificial neural networks and regression techniques - Muhammad Hassan; Muhammad Ali Shamim; Hashim Nisar Hashmi; Syed Zishan Ashiq; Imtiaz Ahmed; Ghufran Ahmed Pasha; Usman Ali Naeem; Abdul Razzaq Ghumman; Dawei Han (Earth Science Informatics, 2015)