{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T16:14:08Z","timestamp":1774023248671,"version":"3.50.1"},"reference-count":65,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100003968","name":"Iran National Science Foundation","doi-asserted-by":"publisher","award":["40403143"],"award-info":[{"award-number":["40403143"]}],"id":[{"id":"10.13039\/501100003968","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Environmental Modelling & Software"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1016\/j.envsoft.2026.106883","type":"journal-article","created":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T17:15:11Z","timestamp":1770225311000},"page":"106883","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Development of a hybrid Bayesian-Hyperband optimization procedure: GeoAI-driven hyperparameter tuning of AdaBoost for enhancing Mineral Prospectivity Mapping"],"prefix":"10.1016","volume":"198","author":[{"given":"Mahsa","family":"Hajihosseinlou","sequence":"first","affiliation":[]},{"given":"Abbas","family":"Maghsoudi","sequence":"additional","affiliation":[]},{"given":"Reza","family":"Ghezelbash","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"4","key":"10.1016\/j.envsoft.2026.106883_bib1","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1016\/j.chemer.2013.07.001","article-title":"An SVM-based machine learning method for the separation of alteration zones in Sungun porphyry copper deposit","volume":"73","author":"Abbaszadeh","year":"2013","journal-title":"Geochemistry"},{"issue":"5","key":"10.1016\/j.envsoft.2026.106883_bib2","article-title":"Fuzzy and machine learning based multi-criteria decision making for selecting electronics product","volume":"10","author":"Agarwal","year":"2023","journal-title":"EAI Endorsed Transactions on Scalable Information Systems"},{"issue":"2","key":"10.1016\/j.envsoft.2026.106883_bib3","first-page":"128","article-title":"Comparative review of the northern Sanandaj-Sirjan zone granitoids","volume":"1","author":"Ahadnejad","year":"2013","journal-title":"J. Tethys"},{"issue":"4","key":"10.1016\/j.envsoft.2026.106883_bib4","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1046\/j.1440-0952.2000.00807.x","article-title":"Artificial neural networks: a new method for mineral prospectivity mapping","volume":"47","author":"Brown","year":"2000","journal-title":"Aust. J. Earth Sci."},{"key":"10.1016\/j.envsoft.2026.106883_bib5","series-title":"Geochemical Anomaly and Mineral Prospectivity Mapping in GIS","author":"Carranza","year":"2008"},{"key":"10.1016\/j.envsoft.2026.106883_bib6","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.cageo.2014.10.004","article-title":"Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines)","volume":"74","author":"Carranza","year":"2015","journal-title":"Comput. Geosci."},{"key":"10.1016\/j.envsoft.2026.106883_bib7","article-title":"Detecting low-risk exploration targets from automated tuned machine learning algorithms: hybrid particle swarm optimization, grid search and genetic optimization algorithms with support vector machine","author":"Daviran","year":"2025","journal-title":"J. Earth Sci."},{"issue":"1","key":"10.1016\/j.envsoft.2026.106883_bib8","doi-asserted-by":"crossref","DOI":"10.1016\/j.chemer.2023.126036","article-title":"GWOKM: a novel hybrid optimization algorithm for geochemical anomaly detection based on grey wolf optimizer and K-means clustering","volume":"84","author":"Daviran","year":"2024","journal-title":"Geochemistry"},{"key":"10.1016\/j.envsoft.2026.106883_bib9","doi-asserted-by":"crossref","DOI":"10.1016\/j.cageo.2024.105785","article-title":"Optimized AI-MPM: application of PSO for tuning the hyperparameters of SVM and RF algorithms","volume":"195","author":"Daviran","year":"2025","journal-title":"Comput. Geosci."},{"issue":"1","key":"10.1016\/j.envsoft.2026.106883_bib10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s40808-024-02176-z","article-title":"Uncertainty quantification in genetic algorithm-optimized artificial intelligence-based mineral prospectivity models: automated hyperparameter tuning for support vector machines and random forest","volume":"11","author":"Daviran","year":"2025","journal-title":"Modeling Earth Systems and Environment"},{"key":"10.1016\/j.envsoft.2026.106883_bib11","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2025.106246","article-title":"Intelligent siltation diagnosis for drainage pipelines using weak-form analysis and theory-guided neural networks in geo-infrastructure","volume":"176","author":"Di","year":"2025","journal-title":"Autom. ConStruct."},{"issue":"2","key":"10.1016\/j.envsoft.2026.106883_bib12","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.jseaes.2009.08.007","article-title":"Emarat carbonate-hosted Zn\u2013Pb deposit, Markazi province, Iran: a geological, mineralogical and isotopic (S, Pb) study","volume":"37","author":"Ehya","year":"2010","journal-title":"J. Asian Earth Sci."},{"key":"10.1016\/j.envsoft.2026.106883_bib13","series-title":"Using SFA and GMPI Methods for Geochemical Exploration of Buried Porphyry Copper Deposits, Case Study: Janja copper-gold Deposit, SE Iran","author":"Esmailzadeh","year":"2025"},{"key":"10.1016\/j.envsoft.2026.106883_bib14","series-title":"2024 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS)","first-page":"1","article-title":"Remote sensing data processing using convolutional neural networks for mapping alteration zones","author":"Farahbakhsh","year":"2024"},{"issue":"4","key":"10.1016\/j.envsoft.2026.106883_bib15","first-page":"945","article-title":"Application of fractal modeling and delineation of hydrothermal alterations in the separation of Zn, Pb, Cu, and Ba anomalies in Varcheh 1: 100000 sheet (south of Arak)","volume":"12","author":"Farhadinejad","year":"2023","journal-title":"Advanced Applied Geology"},{"key":"10.1016\/j.envsoft.2026.106883_bib16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12517-017-3209-4","article-title":"Designing infill directional drilling in mineral exploration by using particle swarm optimization algorithm","volume":"10","author":"Fatehi","year":"2017","journal-title":"Arabian J. Geosci."},{"key":"10.1016\/j.envsoft.2026.106883_bib17","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1007\/s00500-008-0392-y","article-title":"A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability","volume":"13","author":"Garc\u00eda","year":"2009","journal-title":"Soft Comput."},{"key":"10.1016\/j.envsoft.2026.106883_bib18","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1016\/j.jafrearsci.2017.07.015","article-title":"Combination of separation methods and data mining techniques for prediction of anomalous areas in Susanvar, central Iran","volume":"134","author":"Ghannadpour","year":"2017","journal-title":"J. Afr. Earth Sci."},{"issue":"6","key":"10.1016\/j.envsoft.2026.106883_bib19","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1016\/j.jseaes.2005.01.003","article-title":"A new tectonic scenario for the Sanandaj\u2013Sirjan zone (Iran)","volume":"26","author":"Ghasemi","year":"2006","journal-title":"J. Asian Earth Sci."},{"issue":"4","key":"10.1016\/j.envsoft.2026.106883_bib20","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1007\/s11053-018-9448-6","article-title":"An improved data-driven multiple criteria decision-making procedure for spatial modeling of mineral prospectivity: adaption of prediction\u2013area plot and logistic functions","volume":"28","author":"Ghezelbash","year":"2019","journal-title":"Nat. Resour. Res."},{"key":"10.1016\/j.envsoft.2026.106883_bib21","doi-asserted-by":"crossref","DOI":"10.1016\/j.cageo.2019.104335","article-title":"Optimization of geochemical anomaly detection using a novel genetic K-means clustering (GKMC) algorithm","volume":"134","author":"Ghezelbash","year":"2020","journal-title":"Comput. Geosci."},{"issue":"2","key":"10.1016\/j.envsoft.2026.106883_bib22","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s12145-025-01708-0","article-title":"Density based spatial clustering of applications with noise and fuzzy C-means algorithms for unsupervised mineral prospectivity mapping","volume":"18","author":"Ghezelbash","year":"2025","journal-title":"Earth Science Informatics"},{"key":"10.1016\/j.envsoft.2026.106883_bib23","article-title":"Combination of machine learning and fractal approaches for AI-MPM: identifying low-risk exploration targets associated with Porphyry-Cu deposits in the Kerman Belt, Iran","author":"Ghezelbash","year":"2025","journal-title":"Remote Sens. Appl.: Society and Environment"},{"key":"10.1016\/j.envsoft.2026.106883_bib24","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.jpsychires.2021.11.029","article-title":"Comparison of traditional model-based statistical methods with machine learning for the prediction of suicide behaviour","volume":"145","author":"Grendas","year":"2022","journal-title":"J. Psychiatr. Res."},{"issue":"8","key":"10.1016\/j.envsoft.2026.106883_bib25","doi-asserted-by":"crossref","first-page":"2518","DOI":"10.1002\/rob.22361","article-title":"An online optimization escape entrapment strategy for planetary rovers based on Bayesian optimization","volume":"41","author":"Guo","year":"2024","journal-title":"J. Field Robot."},{"key":"10.1016\/j.envsoft.2026.106883_bib26","doi-asserted-by":"crossref","DOI":"10.1016\/j.chemer.2024.126094","article-title":"A comprehensive evaluation of OPTICS, GMM and K-means clustering methodologies for geochemical anomaly detection connected with sample catchment basins","author":"Hajihosseinlou","year":"2024","journal-title":"Geochemistry"},{"issue":"5","key":"10.1016\/j.envsoft.2026.106883_bib27","doi-asserted-by":"crossref","first-page":"4859","DOI":"10.1007\/s12145-024-01404-5","article-title":"Regularization in machine learning models for MVT Pb-Zn prospectivity mapping: applying lasso and elastic-net algorithms","volume":"17","author":"Hajihosseinlou","year":"2024","journal-title":"Earth Science Informatics"},{"issue":"12","key":"10.1016\/j.envsoft.2026.106883_bib28","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.3390\/min14121202","article-title":"Geochemical anomaly detection and pattern recognition: a combined study of the Apriori algorithm, principal component analysis, and spectral clustering","volume":"14","author":"Hajihosseinlou","year":"2024","journal-title":"Minerals"},{"key":"10.1016\/j.envsoft.2026.106883_bib75","doi-asserted-by":"crossref","first-page":"107755","DOI":"10.1016\/j.gexplo.2025.107755","article-title":"A semi-supervised learning framework for intelligent mineral prospectivity mapping: incorporation of the CatBoost and Gaussian mixture model algorithms","volume":"274","author":"Hajihosseinlou","year":"2025","journal-title":"J. Geochem. Explor."},{"key":"10.1016\/j.envsoft.2026.106883_bib76","doi-asserted-by":"crossref","first-page":"106783","DOI":"10.1016\/j.oregeorev.2025.106783","article-title":"A semi-supervised approach for mineral prospectivity mapping via weighted positive-unlabeled learning and tree-structured parzen estimator for hyperparameter optimization","author":"Hajihosseinlou","year":"2025","journal-title":"Ore Geol. Rev."},{"key":"10.1016\/j.envsoft.2026.106883_bib31","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/A:1011548709573","article-title":"Application of GIS processing techniques for producing mineral prospectivity maps\u2014a case study: mesothermal Au in the Swayze Greenstone Belt, Ontario, Canada","volume":"10","author":"Harris","year":"2001","journal-title":"Nat. Resour. Res."},{"key":"10.1016\/j.envsoft.2026.106883_bib32","series-title":"Hyperparameter Optimization: a Spectral Approach","author":"Hazan","year":"2017"},{"key":"10.1016\/j.envsoft.2026.106883_bib33","series-title":"Opportunities and Challenges from Artificial Intelligence and Machine Learning for the Advancement of Science, Technology, and the Office of Science Missions","author":"Hey","year":"2020"},{"issue":"3","key":"10.1016\/j.envsoft.2026.106883_bib34","doi-asserted-by":"crossref","first-page":"255","DOI":"10.2747\/0020-6814.48.3.255","article-title":"Fluid inclusion investigations of the Raigan porphyry copper system, Kerman-Bam, Iran","volume":"48","author":"Hezarkhani","year":"2006","journal-title":"Int. Geol. Rev."},{"issue":"4","key":"10.1016\/j.envsoft.2026.106883_bib35","doi-asserted-by":"crossref","DOI":"10.1016\/j.chemer.2024.126208","article-title":"Deep embedded clustering: delineating multivariate geochemical anomalies in the Feizabad region","volume":"84","author":"Hoseinzade","year":"2024","journal-title":"Geochemistry"},{"issue":"3","key":"10.1016\/j.envsoft.2026.106883_bib36","doi-asserted-by":"crossref","first-page":"2489","DOI":"10.1007\/s11069-021-04929-w","article-title":"Application of prediction\u2013area plot in the assessment of MCDM methods through VIKOR, PROMETHEE II, and permutation","volume":"109","author":"Hoseinzade","year":"2021","journal-title":"Nat. Hazards"},{"issue":"4","key":"10.1016\/j.envsoft.2026.106883_bib37","doi-asserted-by":"crossref","DOI":"10.1142\/S021987622450066X","article-title":"Machine learning\u2013finite element mesh optimization-based modeling and prediction of excavation-induced shield tunnel ground settlement","volume":"22","author":"Hu","year":"2025","journal-title":"Int. J. Comput. Methods"},{"issue":"3","key":"10.1016\/j.envsoft.2026.106883_bib39","first-page":"502","article-title":"Identification of hidden mineral deposits using airborne magnetic data in the Malayer-Esfahan metallogenic belt (MEMB), Iran","volume":"12","author":"Jafari","year":"2022","journal-title":"Advanced Applied Geology"},{"issue":"1","key":"10.1016\/j.envsoft.2026.106883_bib40","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1214\/aos\/1079120128","article-title":"Process consistency for adaboost","volume":"32","author":"Jiang","year":"2004","journal-title":"Ann. Stat."},{"key":"10.1016\/j.envsoft.2026.106883_bib41","doi-asserted-by":"crossref","DOI":"10.1016\/j.petrol.2020.107388","article-title":"Modeling viscosity of light and intermediate dead oil systems using advanced computational frameworks and artificial neural networks","volume":"193","author":"Khamehchi","year":"2020","journal-title":"J. Petrol. Sci. Eng."},{"issue":"8","key":"10.1016\/j.envsoft.2026.106883_bib42","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.3390\/min13081073","article-title":"Comparative study of random forest and support vector machine algorithms in mineral prospectivity mapping with limited training data","volume":"13","author":"Lachaud","year":"2023","journal-title":"Minerals"},{"key":"10.1016\/j.envsoft.2026.106883_bib43","doi-asserted-by":"crossref","DOI":"10.1016\/j.ndteint.2024.103043","article-title":"GPR detection localization of underground structures based on deep learning and reverse time migration","volume":"143","author":"Lei","year":"2024","journal-title":"NDT E Int."},{"key":"10.1016\/j.envsoft.2026.106883_bib44","series-title":"Grid Search, Random Search, Genetic Algorithm: a Big Comparison for NAS","author":"Liashchynskyi","year":"2019"},{"key":"10.1016\/j.envsoft.2026.106883_bib45","doi-asserted-by":"crossref","DOI":"10.1016\/j.oregeorev.2021.104262","article-title":"Genetic model for Jurassic shale-hosted Zn-Pb deposits of the Arak mining district, Malayer-Esfahan metallogenic belt: insight from sedimentological, textural, and stable isotope characteristics","volume":"136","author":"Mahmoodi","year":"2021","journal-title":"Ore Geol. Rev."},{"issue":"3","key":"10.1016\/j.envsoft.2026.106883_bib46","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0194889","article-title":"Statistical and machine learning forecasting methods: concerns and ways forward","volume":"13","author":"Makridakis","year":"2018","journal-title":"PLoS One"},{"key":"10.1016\/j.envsoft.2026.106883_bib47","doi-asserted-by":"crossref","DOI":"10.1016\/j.oregeorev.2025.106458","article-title":"Simulation-based mineral prospectivity modeling and gray wolf optimization algorithm for delimiting exploration targets","author":"Mostafaei","year":"2025","journal-title":"Ore Geol. Rev."},{"issue":"1","key":"10.1016\/j.envsoft.2026.106883_bib48","first-page":"52","article-title":"Diagenesis and tectonic setting of the varcheh intrusive masses in sanandaj-sirjan zone, Iran","volume":"12","author":"Naeemi","year":"2022","journal-title":"Revista Geoaraguaia"},{"key":"10.1016\/j.envsoft.2026.106883_bib50","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1023\/A:1025171803637","article-title":"Artificial neural networks for mineral-potential mapping: a case study from Aravalli province, Western India","volume":"12","author":"Porwal","year":"2003","journal-title":"Nat. Resour. Res."},{"key":"10.1016\/j.envsoft.2026.106883_bib51","doi-asserted-by":"crossref","DOI":"10.3389\/fmars.2025.1636124","article-title":"Physics-guided deep neural networks for bathymetric mapping using Sentinel-2 multi-spectral imagery","volume":"12","author":"Qian","year":"2025","journal-title":"Front. Mar. Sci."},{"key":"10.1016\/j.envsoft.2026.106883_bib54","first-page":"1","article-title":"Recognition of multi-element geochemical anomalies related to Pb\u2013Zn mineralization applying upgraded support vector machine in the Varcheh district of Iran","author":"Sabbaghi","year":"2024","journal-title":"Modeling Earth Systems and Environment"},{"key":"10.1016\/j.envsoft.2026.106883_bib55","series-title":"Quantification of Uncertainty in Geochemical Anomalies in Mineral Exploration","author":"Sadeghi","year":"2020"},{"issue":"4","key":"10.1016\/j.envsoft.2026.106883_bib57","doi-asserted-by":"crossref","DOI":"10.1016\/j.chemer.2024.126207","article-title":"A hybrid framework for detection of multivariate porphyry Cu signatures and anomaly enhancement: incorporation of SFA, GMPI, and grey wolf optimization","volume":"84","author":"Saremi","year":"2024","journal-title":"Geochemistry"},{"issue":"4","key":"10.1016\/j.envsoft.2026.106883_bib58","doi-asserted-by":"crossref","first-page":"411","DOI":"10.3390\/min15040411","article-title":"Unsupervised anomaly detection for mineral prospectivity mapping using isolation forest and extended isolation forest algorithms","volume":"15","author":"Saremi","year":"2025","journal-title":"Minerals"},{"key":"10.1016\/j.envsoft.2026.106883_bib59","doi-asserted-by":"crossref","DOI":"10.1016\/j.chemer.2025.126337","article-title":"Analysis of clustering methods for geochemical anomaly identification through weighted sample catchment basins","author":"Shirjang","year":"2025","journal-title":"Geochemistry"},{"key":"10.1016\/j.envsoft.2026.106883_bib60","doi-asserted-by":"crossref","DOI":"10.1016\/j.oceaneng.2024.118672","article-title":"Theoretical and numerical methods for predicting the structural stiffness of unbonded flexible riser for deep-sea mining under axial tension and internal pressure","volume":"310","author":"Sun","year":"2024","journal-title":"Ocean Eng."},{"issue":"3","key":"10.1016\/j.envsoft.2026.106883_bib61","first-page":"408","article-title":"Application of adaptive neuro-fuzzy inference system for grade estimation; case study, Sarcheshmeh porphyry copper deposit, Kerman, Iran","volume":"4","author":"Tahmasebi","year":"2010","journal-title":"Aust. J. Basic Appl. Sci."},{"key":"10.1016\/j.envsoft.2026.106883_bib62","article-title":"Anomaly detection in hyperspectral images using adaptive graph frequency location","author":"Tu","year":"2024","journal-title":"IEEE Transact. Neural Networks Learn. Syst."},{"issue":"12","key":"10.1016\/j.envsoft.2026.106883_bib74","doi-asserted-by":"crossref","first-page":"2233","DOI":"10.1016\/j.apgeochem.2005.08.009","article-title":"Sequential factor analysis as a new approach to multivariate analysis of heterogeneous geochemical datasets: an application to a bulk chemical characterization of fluvial deposits (Rhine\u2013Meuse delta, The Netherlands)","volume":"20","author":"Van Helvoort","year":"2005","journal-title":"Appl. Geochem."},{"key":"10.1016\/j.envsoft.2026.106883_bib63","doi-asserted-by":"crossref","DOI":"10.1016\/j.oregeorev.2023.105468","article-title":"LA-ICP-MS analyses of trace elements in zoned sphalerite: a study from the Maoping carbonate-hosted Pb-Zn (-Ge) deposit, southwest China","volume":"157","author":"Wang","year":"2023","journal-title":"Ore Geol. Rev."},{"issue":"6","key":"10.1016\/j.envsoft.2026.106883_bib67","first-page":"745","article-title":"Advance and prospects of AdaBoost algorithm","volume":"39","author":"Ying","year":"2013","journal-title":"Acta Autom. Sin."},{"key":"10.1016\/j.envsoft.2026.106883_bib68","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s11053-014-9261-9","article-title":"Data-driven index overlay and Boolean logic mineral prospectivity modeling in greenfields exploration","volume":"25","author":"Yousefi","year":"2016","journal-title":"Nat. Resour. Res."},{"key":"10.1016\/j.envsoft.2026.106883_bib69","doi-asserted-by":"crossref","DOI":"10.1109\/TGRS.2024.3452038","article-title":"Real-time joint filtering of gravity and gravity gradient data based on improved Kalman filter","author":"Yuan","year":"2024","journal-title":"IEEE Trans. Geosci. Rem. Sens."},{"issue":"6","key":"10.1016\/j.envsoft.2026.106883_bib70","doi-asserted-by":"crossref","first-page":"2463","DOI":"10.1007\/s11053-023-10268-3","article-title":"Integration of ASTER and soil survey data by principal components analysis and one-class support vector machine for mineral prospectivity mapping in Kerkasha, Southwestern Eritrea","volume":"32","author":"Zerai","year":"2023","journal-title":"Nat. Resour. Res."},{"key":"10.1016\/j.envsoft.2026.106883_bib71","article-title":"S2DBFT: spectral-spatial dual-branch fusion transformer for hyperspectral image classification","author":"Zhang","year":"2025","journal-title":"IEEE Trans. Geosci. Rem. Sens."},{"issue":"12","key":"10.1016\/j.envsoft.2026.106883_bib73","doi-asserted-by":"crossref","first-page":"1967","DOI":"10.1016\/j.cageo.2010.09.014","article-title":"Support vector machine: a tool for mapping mineral prospectivity","volume":"37","author":"Zuo","year":"2011","journal-title":"Comput. Geosci."}],"container-title":["Environmental Modelling & Software"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1364815226000307?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1364815226000307?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T15:31:29Z","timestamp":1774020689000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1364815226000307"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":65,"alternative-id":["S1364815226000307"],"URL":"https:\/\/doi.org\/10.1016\/j.envsoft.2026.106883","relation":{},"ISSN":["1364-8152"],"issn-type":[{"value":"1364-8152","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Development of a hybrid Bayesian-Hyperband optimization procedure: GeoAI-driven hyperparameter tuning of AdaBoost for enhancing Mineral Prospectivity Mapping","name":"articletitle","label":"Article Title"},{"value":"Environmental Modelling & Software","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.envsoft.2026.106883","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"106883"}}