Acharya, A., Steiner, J. F., Walizada, K. M., Ali, S., Zakir, Z. H., Caiserman, A., and Watanabe, T.: Review article: Snow and ice avalanches in high mountain Asia – scientific, local and indigenous knowledge, Nat. Hazards Earth Syst. Sci., 23, 2569–2592, https://doi.org/10.5194/nhess-23-2569-2023, 2023. a
Afendras, G. and Markatou, M.: Optimality of training/test size and resampling effectiveness in cross-validation, Journal of Statistical Planning and Inference, 199, https://doi.org/10.1016/j.jspi.2018.07.005, 2019. a
Ahsan, M., Khan, A., Khan, K. R., Sinha, B. B., and Sharma, A.: Advancements in medical diagnosis and treatment through machine learning: a review, Expert Syst., 41, e13499, https://doi.org/10.1111/exsy.13499, 2024. a
Amin, G., Imtiaz, I., Haroon, E., Saqib, N. U., Shahzad, M. I., and Nazeer, M.: Assessment of machine learning algorithms for land cover classification in a complex mountainous landscape, Journal of Geovisualization and Spatial Analysis, 8, 34, https://doi.org/10.1007/s41651-024-00195-z, 2024. a
Bakkehøi, S., Domaas, U., and Lied, K.: Calculation of snow avalanche runout distance, Ann. Glaciol., 4, 24–29, https://doi.org/10.3189/S0260305500005188, 1983. a
Bhattacharyya, S.: Confidence in predictions from random tree ensembles, in: 2011 IEEE 11th International Conference on Data Mining, iSSN 2374–8486, https://doi.org/10.1109/ICDM.2011.41, 71–80, 2011. a
Bischl, B., Binder, M., Lang, M., Pielok, T., Richter, J., Coors, S., Thomas, J., Ullmann, T., Becker, M., Boulesteix, A.-L., Deng, D., and Lindauer, M.: Hyperparameter optimization: foundations, algorithms, best practices, and open challenges, WIREs Data Mining and Knowledge Discovery, 13, e1484, https://doi.org/10.1002/widm.1484, 2023. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. a, b, c
Bühler, Y., Kumar, S., Veitinger, J., Christen, M., Stoffel, A., and Snehmani: Automated identification of potential snow avalanche release areas based on digital elevation models, Nat. Hazards Earth Syst. Sci., 13, 1321–1335, https://doi.org/10.5194/nhess-13-1321-2013, 2013. a, b
Bühler, Y., von Rickenbach, D., Stoffel, A., Margreth, S., Stoffel, L., and Christen, M.: Automated snow avalanche release area delineation – validation of existing algorithms and proposition of a new object-based approach for large-scale hazard indication mapping, Nat. Hazards Earth Syst. Sci., 18, 3235–3251, https://doi.org/10.5194/nhess-18-3235-2018, 2018. a, b
Campbell, C. and Gould, B.: A proposed practical model for zoning with the avalanche terrain exposure scale, in: Proceedings, International Snow Science Workshop 2013, Grenoble, France, https://arc.lib.montana.edu/snow-science/objects/ISSW13_paper_P5-02.pdf (last access: 1 April 2025), 2013. a
Cetinkaya, S. and Kocaman, S.: IMPACT OF LEARNING SET AND SAMPLING FOR SNOW AVALANCHE SUSCEPTIBILITY MAPPING WITH RANDOM FOREST, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-1-2023, 57–64, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-57-2023, 2023. a
Contreras, P., Orellana-Alvear, J., Muñoz, P., Bendix, J., and Célleri, R.: Influence of random forest hyperparameterization on short-term runoff forecasting in an Andean mountain catchment, Atmosphere-Basel, 12, 238, https://doi.org/10.3390/atmos12020238, 2021. a, b
Copernicus: Tree Cover Density 2018 (raster 10 m and 100 m), Europe, 3-yearly, European Environment Agency (EEA) [data set], https://doi.org/10.2909/486f77da-d605-423e-93a9-680760ab6791, 2020. a
D’Amboise, C., Teich, M., Hormes, A., Steger, S., and Berger, F.: Modeling protective forests for gravitational natural hazards and how it relates to risk-based decision support tools, in: Protective Forests as Ecosystem-based Solution for Disaster Risk Reduction (ECO-DRR), IntechOpen, London, https://doi.org/10.5772/intechopen.99510, 2021. a, b
D’Amboise, C. J. L., Neuhauser, M., Teich, M., Huber, A., Kofler, A., Perzl, F., Fromm, R., Kleemayr, K., and Fischer, J.-T.: Flow-Py v1.0: a customizable, open-source simulation tool to estimate runout and intensity of gravitational mass flows, Geosci. Model Dev., 15, 2423–2439, https://doi.org/10.5194/gmd-15-2423-2022, 2022. a, b, c, d, e
Dankan Gowda, V., Pathak, D., Prasad, K. D. V., Srinivas, V., Manu, Y. M., and Sudhakar Reddy, N.: Scalable machine learning frameworks for large-scale multimodal image and speech signal processing, in: 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), ISSN 2768-0673, https://doi.org/10.1109/I-SMAC61858.2024.10714812, 1693–1699, 2024. a
Dutta, S., Arunachalam, A., and Misailovic, S.: To seed or not to seed? An empirical analysis of usage of seeds for testing in machine learning projects, in: 2022 IEEE Conference on Software Testing, Verification and Validation (ICST), IEEE, Valencia, Spain, https://doi.org/10.1109/ICST53961.2022.00026, 151–161, 2022. a
Engeset, R. V., Pfuhl, G., Landrø, M., Mannberg, A., and Hetland, A.: Communicating public avalanche warnings – what works?, Nat. Hazards Earth Syst. Sci., 18, 2537–2559, https://doi.org/10.5194/nhess-18-2537-2018, 2018. a
Gavaldã, J., Moner, I., and Bacardit, M.: Integrating the ATES into the avalanche information in Aran Valley (Central Pyrenees), in: Proceedings, International Snow Science Workshop 2013, Grenoble, France, https://arc.lib.montana.edu/snow-science/objects/ISSW13_paper_P5-01.pdf (1 May 2025), 2013. a
Gong, Y., Liu, G., Xue, Y., Li, R., and Meng, L.: A survey on dataset quality in machine learning, Inform. Software Tech., 162, https://doi.org/10.1016/j.infsof.2023.107268, 2023. a
Harvey, S., Schmudlach, G., Bühler, Y., Dürr, L., Stoffel, A., and Christen, M.: Avalanche terrain maps for backcountry skiing in Switzerland, in: Proceedings, International Snow Science Workshop, Innsbruck, Austria, Innsbruck, Austria, 1625–1631, https://arc.lib.montana.edu/snow-science/objects/ISSW2018_O19.1.pdf (last access: 12 February 2025), 2018. a
Harvey, S., Christen, M., Bühler, Y., Hänni, C., Boos, N., and Bernegger, B.: Refined Swiss avalanche terrain mapping CATV2/ATHV2, in: Proceedings, International Snow Science Workshop, Tromsø, Norway, 2024, Tromsø, Norway, 1637–1644, https://arc.lib.montana.edu/snow-science/item/3363 (last access: 17 April 2025), 2024. a
Hesselbach, C.: Adaptation and Application of an Automated Avalanche Terrain Classification in Austria, Master’s thesis, University of Natural Resources and Life Sciences, Vienna, https://permalink.obvsg.at/bok/AC16964320 (last access: 22 April 2025), 2023. a, b, c
Horn, B. K. P.: Hillshading and the reflectance map, P. IEEE, 69, 14–47, https://doi.org/10.1109/proc.1981.11918, 1981. a
Huber, A., Hesselbach, C., Oesterle, F., Neuhauser, M., Adams, M., Plörer, M., Stephan, L., Toft, H., Sykes, J., Mitterer, C., and Fischer, J.-T.: AutoATES Austria – testing and application of an automated model-chain for avalanche terrain classification in the Austrian Alps, in: Proceedings, International Snow Science Workshop, Bend, OR, USA, 2023, Innsbruck, Austria, 1272–1278, http://arc.lib.montana.edu/snow-science/item/2989 (last access: 16 March 2025), 2023. a, b, c, d, e, f, g, h, i
Huber, A., Saxer, L., Spannring, P., Hesselbach, C., Neuhauser, M., D’Amboise, C., and Teich, M.: Regional-scale avalanche modeling with com4FlowPy – potential and limitations for considering avalanche-forest interaction along the avalanche track, in: Proceedings, International Snow Science Workshop, Tromsø, Norway, 2024, 587–594, https://arc.lib.montana.edu/snow-science/item.php?id=3194 (last access: 10 February 2025), 2024. a, b, c, d
Jaiswal, J. K. and Samikannu, R.: Application of random forest algorithm on feature subset selection and classification and regression, in: World Congress on Computing and Communication Technologies (WCCCT), https://doi.org/10.1109/wccct.2016.25, 2017. a
Japkowicz, N.: Concept-learning in the presence of between-class and within-class imbalances, in: Advances in Artificial Intelligence, edited by: Stroulia, E. and Matwin, S., Springer, Berlin, Heidelberg, https://doi.org/10.1007/3-540-45153-6_7, 67–77, 2001. a
Joseph, V. R.: Optimal ratio for data splitting, Statistical Analysis and Data Mining: The ASA Data Science Journal, 15, 531–538, https://doi.org/10.1002/sam.11583, 2022. a
Körner, H. J.: The energy-line method in the mechanics of avalanches, J. Glaciol., 26, 501–505, https://doi.org/10.3189/S0022143000011023, 1980. a
Larsen, H. T., Hendrikx, J., Slåtten, M. S., and Engeset, R. V.: Developing nationwide avalanche terrain maps for Norway, Nat. Hazards, 103, 2829–2847, https://doi.org/10.1007/s11069-020-04104-7, 2020. a, b, c, d, e, f, g, h
Markov, K.: kalinmarkov95/machine-learning-auto-ates: Implement Random Forest (RF) for automated ATES classification, Zenodo [code], https://doi.org/10.5281/zenodo.15310357, 2025. a, b
Markov, K. and Ivanov, I.: Avalanche Hazard Assessment and Modeling in Pirin National Park, LOPS Foundation, Sofia, Bulgaria, https://lopsbg.com/wp-content/uploads/2021/09/%D0%9A%D0%B0%D0%BB%D0%B8%D0%BD-%D0%9C%D0%B0%D1%80%D0%BA%D0%BE%D0%B2_%D0%98%D0%B2%D0%B0%D0%BD-%D0%98%D0%B2%D0%B0%D0%BD%D0%BE%D0%B2.pdf (last access: 30 March 2025), 2021. a
Markov, K. and Panayotov, M.: Defining of avalanche terrain with ATES modelling for the region of Bansko Ski Resort in Pirin, in: Avalanches in the Bunderitsa valley in Pirin Mountains, Intel Entrans, Sofia, ISBN 978-619-7703-60-3, 2024. a, b
Markov, K., Panayotov, M., Tcherkezova, E., and Teich, M.: Identifying and mapping avalanche terrain using the ATES model for the region around Bansko Ski Resort, Pirin, Forest Science (Nauka za Gorata), 60, 99–122, 2024. a
Markov, K., Panayotov, M., Tcherkezova, E., and Huber, A.: Influence of forest canopy cover on automated Avalanche Terrain Exposure Scale classification in the Pirin Mountains, Bulgaria, Forestry Ideas, 31, 170–187, https://forestry-ideas.info/issues/issues_Download.php?download=557 (last access: 30 October 2025), 2025. a
McClung, D. and Gauer, P.: Maximum frontal speeds, alpha angles and deposit volumes of flowing snow avalanches, Cold Reg. Sci. Technol., 153, 78–85, https://doi.org/10.1016/j.coldregions.2018.04.009, 2018. a
Nasteski, V.: An overview of the supervised machine learning methods, Horizons, 4, 51–62, https://www.researchgate.net/publication/328146111_An_overview_of_the_supervised_machine_learning_methods (last access: 29 October 2025), 2017. a, b
Oesterle, F., Wirbel, A., Fischer, J.-T., Huber, A., and Spannring, P.: avaframe/AvaFrame: 1.11, Zenodo [code], https://doi.org/10.5281/zenodo.14893015, 2025. a
Panayotov, M. and Tsvetanov, N.: Dating of avalanches in Pirin mountains in Bulgaria by tree-ring analysis of Pinus peuce and Pinus heldriechii trees, Dendrochronologia, 85, 126206, https://doi.org/10.1016/j.dendro.2024.126206, 2024. a
Panayotov, M., Menteshev, B., and Mihaylova, S.: Avalanches in Bulgaria, BEFSA, ISBN 978-619-188-515-2, 2021. a, b, c
Panayotov, M., Christen, M., Bebi, P., and Bartelt, P.: Simulation of avalanches in Bunderitsa valley with the RAMMS software, in: Avalanches in the Bunderitsa valley in Pirin Mountains, Intel Entrans, Sofia, ISBN 978-619-7703-60-3, 2024a. a
Panayotov, M., Markov, K., Tsvetanov, N., Huber, A., Hesselbach, C., and Teich, M.: Avalanche hazard mapping using the avalanche terrain exposure scale (ATES) in the high mountain ranges of Bulgaria, in: Proceedings, International Snow Science Workshop 2024, Tromsø, Norway, https://arc.lib.montana.edu/snow-science/objects/ISSW2024_P12.5.pdf (last access: 21 April 2025), 2024b. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r
Pandey, S., Chand, S., Horkoff, J., Staron, M., Ochodek, M., and Durisic, D.: Design pattern recognition: a study of large language models, Empir. Softw. Eng., 30, https://doi.org/10.1007/s10664-025-10625-1, 2025. a
Parks Canada Agency: Avalanche Terrain Exposure Scale – Avalanche Terrain Exposure Scale, Government of Canada, https://parks.canada.ca/pn-np/bc/glacier/visit/hiver-winter/ski/ates (last access: 29 October 2025), 2017. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.: Scikit-learn: machine learning in Python, J. Mach. Learn. Res, 12, 2825–2830, 2011. a
Pirin National Park: Pirin National Park – World Heritage Property of Unesco, https://www.pirin.bg/?p=5591#more-5591 (last access: 28 August 2025), 2022. a, b
Prasetiyowati, M. I., Maulidevi, N. U., and Surendro, K.: Determining threshold value on information gain feature selection to increase speed and prediction accuracy of random forest, Journal of Big Data, 8, 84, https://doi.org/10.1186/s40537-021-00472-4, 2021. a
Probst, P., Wright, M., and Boulesteix, A.-L.: Hyperparameters and tuning strategies for random forest, WIREs Data Mining and Knowledge Discovery, 9, e1301, https://doi.org/10.1002/widm.1301, 2019. a, b
Pugliese, R., Regondi, S., and Marini, R.: Machine learning-based approach: global trends, research directions, and regulatory standpoints, Data Science and Management, 4, 19–29, https://doi.org/10.1016/j.dsm.2021.12.002, 2021. a
Python Software Foundation: Python Language Reference, version 3.9.21 [code], https://www.python.org/ (last access: 22 March 2025), 2024. a
Rainio, O., Teuho, J., and Klén, R.: Evaluation metrics and statistical tests for machine learning, Sci. Rep.-UK, 14, 6086, https://doi.org/10.1038/s41598-024-56706-x, 2024. a, b, c
Ramadhan, M., Sitanggang, I., Nasution, F., and Ghifari, A.: Parameter tuning in random forest based on grid search method for gender classification based on voice frequency, DEStech Transactions on Computer Science and Engineering, https://doi.org/10.12783/dtcse/cece2017/14611, 2017. a
Rimal, Y., Sharma, N., and Alsadoon, A.: The accuracy of machine learning models relies on hyperparameter tuning: student result classification using random forest, randomized search, grid search, bayesian, genetic, and optuna algorithms, Multimed. Tools Appl., 83, 74349–74364, https://doi.org/10.1007/s11042-024-18426-2, 2024. a
Rogers, J. and Gunn, S.: Identifying feature relevance using a random forest, in: SLSFS: International Statistical and Optimization Perspectives Workshop “Subspace, Latent Structure and Feature Selection”, edited by: Saunders, C., Grobelnik, M., Gunn, S., and Shawe-Taylor, J., Springer, Berlin, Heidelberg, https://doi.org/10.1007/11752790_12, 173–184, 2006. a
Salman, H. A., Kalakech, A., and Steiti, A.: Random forest algorithm overview, Babylonian Journal of Machine Learning, 2024, 69–79, https://doi.org/10.58496/BJML/2024/007, 2024. a, b
Saraswat, P.: Supervised machine learning algorithm: a review of classification techniques, Smart Innovation, Systems and Technologies, 273, 477–482, https://doi.org/10.1007/978-3-030-92905-3_58, 2022. a
Sarker, I. H.: Machine learning: algorithms, real-world applications and research directions, SN Computer Science, 2, 160, https://doi.org/10.1007/s42979-021-00592-x, 2021. a
Schmudlach, G. and Köhler, J.: Method for an automatized avalanche terrain classification, in: Proceedings, International Snow Science Workshop, Breckenridge, Colorado, 2016, Breckenridge, Colorado, 729–736, https://arc.lib.montana.edu/snow-science/objects/ISSW16_P2.04.pdf (last access: 7 April 2025), 2016. a
Schumacher, J., Toft, H., McLean, J. P., Hauglin, M., Astrup, R., and Breidenbach, J.: The utility of forest attribute maps for automated Avalanche Terrain Exposure Scale (ATES) modelling, Scand. J. Forest Res., 37, 264–275, https://doi.org/10.1080/02827581.2022.2096921, 2022. a, b, c, d, e
Schweizer, J. and Lüschg, M.: Characteristics of human-triggered avalanches, Cold Reg. Sci. Technol., 33, 147–162, https://doi.org/10.1016/S0165-232X(01)00037-4, 2001. a
Schweizer, J., Bartelt, P., and van Herwijnen, A.: Snow avalanches in: Snow and Ice-Related Hazards, Risks and Disasters, Elsevier, https://doi.org/10.1016/b978-0-12-394849-6.00012-3, 395–436, 2015. a
Sharp, A. E. A.: Evaluating the exposure of heliskiing ski guides to avalanche terrain using a fuzzy logic avalanche susceptibility model, Masters’ thesis, University of Leeds, School of Geography, https://doi.org/10.13140/RG.2.2.18673.94567, 2018. a
Sharp, E., Campbell, C., Statham, G., and Schroers, B.: A standardized, multiscale, fuzzy spatialdata model for a Avalanche Terrain Exposure Scale mapping, in: Proceedings, International Snow Science Workshop, Bend, Oregon, 2023, 838–845, http://arc.lib.montana.edu/snow-science/item/2974 (last access: 5 April 2025), 2023. a
Siji George, G. C. and Sumathi, B.: Grid search tuning of hyperparameters in random forest classifier for customer feedback sentiment prediction, International Journal of Advanced Computer Science and Applications, 11, https://doi.org/10.14569/IJACSA.2020.0110920, 2020. a
Spannring, P.: Comparison of two avalanche terrain classification approaches: Avalanche Terrain Exposure Scale – Classified Avalanche Terrain, Master’s thesis, University of Innsbruck, Faculty of Geo- and Atmospheric Sciences, https://resolver.obvsg.at/urn:nbn:at:at-ubi:1-155858 (last access: 30 October 2025), 2024. a
Statham, G. and Campbell, C.: The Avalanche Terrain Exposure Scale (ATES) v.2, Nat. Hazards Earth Syst. Sci., 25, 1113–1137, https://doi.org/10.5194/nhess-25-1113-2025, 2025. a, b, c, d, e, f
Statham, G., McMahon, B., and Tomm, I.: The Avalanche Terrain Exposure Scale, in: Proceedings, International Snow Science Workshop 2006, Telluride, Colorado, https://arc.lib.montana.edu/snow-science/objects/issw-2006-491-497.pdf (last access: 5 May 2025), 2006. a, b, c
Sykes, J., Toft, H., Haegeli, P., and Statham, G.: Automated Avalanche Terrain Exposure Scale (ATES) mapping – local validation and optimization in western Canada, Nat. Hazards Earth Syst. Sci., 24, 947–971, https://doi.org/10.5194/nhess-24-947-2024, 2024. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q
Techel, F. and Zweifel, B.: Recreational avalanche accidents in Switzerland: trends and patterns with an emphasis on burial, rescue methods and avalanche danger, in: Proceedings International Snow Science Workshop 2013, Grenoble, France, https://arc.lib.montana.edu/snow-science/item.php?id=1844 (last access: 4 April 2025), 2013. a
Techel, F., Jarry, F., Kronthaler, G., Mitterer, S., Nairz, P., Pavšek, M., Valt, M., and Darms, G.: Avalanche fatalities in the European Alps: long-term trends and statistics, Geogr. Helv., 71, 147–159, https://doi.org/10.5194/gh-71-147-2016, 2016. a
Toft, H. B., Sykes, J., Schauer, A., Hendrikx, J., and Hetland, A.: AutoATES v2.0: Automated Avalanche Terrain Exposure Scale mapping, Nat. Hazards Earth Syst. Sci., 24, 1779–1793, https://doi.org/10.5194/nhess-24-1779-2024, 2024. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y
Tonnel, M., Wirbel, A., Oesterle, F., and Fischer, J.-T.: AvaFrame com1DFA (v1.3): a thickness-integrated computational avalanche module – theory, numerics, and testing, Geosci. Model Dev., 16, 7013–7035, https://doi.org/10.5194/gmd-16-7013-2023, 2023. a
Tsvetanov, N. and Panayotov, M.: Dendrochronological reconstruction of large magnitude avalanches in Bunderitsa valley, in: Avalanches in the Bunderitsa valley in Pirin Mountains, Intel Entrans, Sofia, ISBN 978-619-7703-60-3, 2024. a, b
Ul Hassan, C. A., Khan, M. S., and Shah, M. A.: Comparison of machine learning algorithms in data classification, in: International Conference on Automation and Computing (ICAC), https://doi.org/10.23919/IConAC.2018.8748995, 2018. a
Varoquaux, G. and Colliot, O.: Evaluating machine learning models and their diagnostic value, in: Machine Learning for Brain Disorders, Springer US, ISBN 978-1-0716-3195-9, 2023. a, b, c
Veitinger, J., Purves, R. S., and Sovilla, B.: Potential slab avalanche release area identification from estimated winter terrain: a multi-scale, fuzzy logic approach, Nat. Hazards Earth Syst. Sci., 16, 2211–2225, https://doi.org/10.5194/nhess-16-2211-2016, 2016a. a, b
Veitinger, J., Sovilla, B., and Purves, R. S.: Model code of release area algorithm [code], https://github.com/jocha81/Avalanche-release (last access: 30 October 2025), 2016b. a, b
Venkadesh, P., S. V., D., Marymariyal, P., and Keerthana, S.: Predicting Natural Disasters With AI and Machine Learning, IGI Global Scientific Publishing, https://doi.org/10.4018/979-8-3693-3362-4.ch003, 2024. a
Viallon-Galinier, L., Hagenmuller, P., and Eckert, N.: Combining modelled snowpack stability with machine learning to predict avalanche activity, The Cryosphere, 17, 2245–2260, https://doi.org/10.5194/tc-17-2245-2023, 2023. a
von Avis, C. D., Sykes, J. M., and Tutt, B.: Development of large scale automated Avalanche Terrain Exposure Scale (ATES) ratings in collaboration with local avalanche experts, in: Proceedings, International Snow Science Workshop 2023, Bend, Oregon, 982–988, http://arc.lib.montana.edu/snow-science/item/2998 (last access: 12 April 2025), 2023. a, b
Wang, S., Zhuang, J., Zheng, J., Fan, H., Kong, J., and Zhan, J.: Application of Bayesian hyperparameter optimized random forest and XGBoost model for landslide susceptibility mapping, Front. Earth Sci., 9, https://doi.org/10.3389/feart.2021.712240, 2021. a
Werners, B.: Aggregation models in mathematical programming, in: Mathematical Models for Decision Support, Vol. 48 of NATO ASI Series, Springer, Berlin, Heidelberg, 295–305, https://doi.org/10.1007/978-3-642-83555-1_19, 1988. a
Yolanda, A., Adnan, A., Ell Goldameir, N., and Rizalde, F.: The comparison of accuracy on classification data with machine learning algorithms (case study: human development index by regency/city in Indonesia 2020), in: AIP Conference Proceedings, https://doi.org/10.1063/5.0118720, 2023. a