{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T16:09:27Z","timestamp":1765296567433,"version":"3.46.0"},"reference-count":44,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T00:00:00Z","timestamp":1765238400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004329","name":"the Ministry of Higher Education, Science and Technology of the Republic of Slovenia","doi-asserted-by":"publisher","award":["P2-0270"],"award-info":[{"award-number":["P2-0270"]}],"id":[{"id":"10.13039\/501100004329","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Operation and maintenance (O&amp;M) events resulting from environmental factors (e.g., precipitation, temperature, seasonality, and unexpected weather conditions) are among the primary sources of operating costs and downtime in run-of-river small hydropower plants (SHPs). This paper presents a data-driven methodology for predicting such long events using machine learning models trained on historical power production, weather radar, and forecast data. Case studies on two Slovenian SHPs with different structural designs and levels of automation demonstrate how environmental features\u2014such as day of year, rain duration, cumulative amount of rain, and rolling precipitation sums\u2014can be used to forecast long events or shutdowns. The proposed approach integrates probabilistic classification outputs with threshold-consistency smoothing to reduce noise and stabilize predictions. Several algorithms were tested\u2014including Logistic Regression, Support Vector Machine (SVM), Random Forest, Gradient Boosting, and k-Nearest Neighbors (k-NN)\u2014across varying feature combinations for O&amp;M model development, with cross-validation ensuring robust evaluation. The models achieved an F1-score of up to 0.58 in SHP1 (k-NN), showing strong seasonality dependence, and up to 0.68 in SHP2 (Gradient Boosting). For SHP1, the best model (k-NN) correctly detected 36 long events, while 15 were misclassified as no events and 38 false alarms were produced. For SHP2, the best model (Gradient Boosting) correctly detected 69 long events, misclassified 23 as no events, and produced 42 false alarms. The findings highlight that probabilistic machine learning-based forecasting can effectively support predictive O&amp;M planning, particularly for manually operated or service-operated SHPs.<\/jats:p>","DOI":"10.3390\/make7040163","type":"journal-article","created":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T15:50:02Z","timestamp":1765295402000},"page":"163","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Prediction of Environment-Related Operation and Maintenance Events in Small Hydropower Plants"],"prefix":"10.3390","volume":"7","author":[{"given":"Luka","family":"Selak","sequence":"first","affiliation":[{"name":"Faculty of Mechanical Engineering, University of Ljubljana, A\u0161ker\u010deva 6, 1000 Ljubljana, Slovenia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4054-7871","authenticated-orcid":false,"given":"Ga\u0161per","family":"\u0160kulj","sequence":"additional","affiliation":[{"name":"Faculty of Mechanical Engineering, University of Ljubljana, A\u0161ker\u010deva 6, 1000 Ljubljana, Slovenia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0093-6979","authenticated-orcid":false,"given":"Dominik","family":"Kozjek","sequence":"additional","affiliation":[{"name":"Faculty of Mechanical Engineering, University of Ljubljana, A\u0161ker\u010deva 6, 1000 Ljubljana, Slovenia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0107-9983","authenticated-orcid":false,"given":"Drago","family":"Bra\u010dun","sequence":"additional","affiliation":[{"name":"Faculty of Mechanical Engineering, University of Ljubljana, A\u0161ker\u010deva 6, 1000 Ljubljana, Slovenia"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,9]]},"reference":[{"key":"ref_1","unstructured":"ESHA (2009). Small Hydropower Roadmap: Condensed Research Data for EU-27. Stream Map Project, ESHA."},{"key":"ref_2","unstructured":"Bureau of Reclamation-Hydroelectric Research and Technical Services Group (2009). Facilities Instructions 4-1A, Maintenance Scheduling for Mechanical Equipment, Bureau of Reclamation-Hydroelectric Research and Technical Services Group."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1080\/03052150701775953","article-title":"Power Plant Maintenance Scheduling Using Ant Colony Optimization: An Improved Formulation","volume":"40","author":"Foong","year":"2008","journal-title":"Eng. Optim."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1093\/ijlct\/ctad043","article-title":"Problems Identification and Performance Analysis in Small Hydropower Plants in Nepal","volume":"18","author":"Pandey","year":"2023","journal-title":"Int. J. Low-Carbon Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.procir.2015.08.101","article-title":"Assessing Feasibility of Operations and Maintenance Automation\u2014A Case of Small Hydropower Plants","volume":"37","author":"Selak","year":"2015","journal-title":"Procedia CIRP"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1016\/S1364-0321(02)00006-0","article-title":"Small Hydro Power: Technology and Current Status","volume":"6","author":"Paish","year":"2002","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1016\/j.rser.2013.05.006","article-title":"Review of Small Hydropower Technology","volume":"26","author":"Okot","year":"2013","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_8","unstructured":"American Society of Civil Engineers (1995). Guidelines for Design of Intakes for Hydroelectric Plants, American Society of Civil Engineers."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1016\/j.rser.2015.04.033","article-title":"Refurbishment and Uprating of Hydro Power Plants\u2014A Literature Review","volume":"48","author":"Rahi","year":"2015","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.geomorph.2016.07.007","article-title":"Effects of Weir Height and Reservoir Widening on Sediment Continuity at Run-of-River Hydropower Plants in Gravel Bed Rivers","volume":"291","author":"Sindelar","year":"2017","journal-title":"Geomorphology"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1016\/j.energy.2013.06.038","article-title":"Mini-Hydro: A Design Approach in Case of Torrential Rivers","volume":"58","author":"Barelli","year":"2013","journal-title":"Energy"},{"key":"ref_12","unstructured":"(2025, June 01). Hydropower Technology: Ossberger. Available online: https:\/\/ossberger.de\/en\/hydropower-technology\/."},{"key":"ref_13","unstructured":"(2025, January 30). Very Low Head Turbine. Available online: http:\/\/www.vlh-turbine.com\/."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.rser.2014.11.045","article-title":"Operation, Performance and Economic Analysis of Low Head Micro-Hydropower Turbines for Rural and Remote Areas: A Review","volume":"43","author":"Elbatran","year":"2015","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Pandey, B., and Karki, A. (2016). Hydroelectric Energy: Renewable Energy and the Environment, CRC Press.","DOI":"10.1201\/9781315374840"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1002\/qre.829","article-title":"Multivariate Statistical Process Control Charts: An Overview","volume":"23","author":"Bersimis","year":"2007","journal-title":"Qual. Reliab. Eng. Int."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"107299","DOI":"10.1016\/j.sigpro.2019.107299","article-title":"Selective Review of Offline Change Point Detection Methods","volume":"167","author":"Truong","year":"2020","journal-title":"Signal Process."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"107714","DOI":"10.1016\/j.ymssp.2021.107714","article-title":"Dynamic Bayesian Monitoring and Detection for Partially Observable Machines under Multivariate Observations","volume":"158","author":"Duan","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"106024","DOI":"10.1016\/j.cie.2019.106024","article-title":"A Systematic Literature Review of Machine Learning Methods Applied to Predictive Maintenance","volume":"137","author":"Carvalho","year":"2019","journal-title":"Comput. Ind. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1016\/j.jhydrol.2016.06.031","article-title":"Value of Different Precipitation Data for Flood Prediction in an Alpine Catchment: A Bayesian Approach","volume":"556","author":"Sikorska","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5989","DOI":"10.1002\/wrcr.20471","article-title":"Evaluation of the Value of Radar QPE Data and Rain Gauge Data for Hydrological Modeling","volume":"49","author":"He","year":"2013","journal-title":"Water Resour. Res."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Di Grande, S., Berlotti, M., Cavalieri, S., and Gueli, R. (2024). A Machine Learning Approach to Forecasting Hydropower Generation. Energies, 17.","DOI":"10.3390\/en17205163"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1016\/S0967-0661(03)00106-0","article-title":"Improvements to the Water Management of a Run-of-River HPP Reservoir: Methodology and Case Study","volume":"12","author":"Paravan","year":"2004","journal-title":"Control Eng. Pract."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liu, Y., Mo, L., Yang, Y., and Tao, Y. (2023). Optimal Scheduling of Cascade Reservoirs Based on an Integrated Multistrategy Particle Swarm Algorithm. Water, 15.","DOI":"10.3390\/w15142593"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1080\/02508060508691835","article-title":"Peak Flow Forecasting with Radar Precipitation and the Distributed Model Casc2d","volume":"30","author":"Jorgeson","year":"2005","journal-title":"Water Int."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2574","DOI":"10.1002\/joc.4158","article-title":"Evaluation of Precipitation in the ENSEMBLES Regional Climate Models over the Complex Orography of Slovenia","volume":"35","author":"Ceglar","year":"2014","journal-title":"Int. J. Climatol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1220","DOI":"10.1016\/j.jhydrol.2017.04.017","article-title":"Using Genetic Algorithms to Optimize the Analogue Method for Precipitation Prediction in the Swiss Alps","volume":"556","author":"Horton","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"67","DOI":"10.5194\/asr-8-67-2012","article-title":"INCA-CE: A Central European Initiative in Nowcasting Severe Weather and Its Applications","volume":"8","author":"Kann","year":"2012","journal-title":"Adv. Sci. Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"257","DOI":"10.5194\/gmd-11-257-2018","article-title":"The ALADIN System and Its Canonical Model Configurations AROME CY41T1 and ALARO-1","volume":"11","author":"Termonia","year":"2018","journal-title":"Geosci. Model Dev."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kumar, A., Islam, T., Sekimoto, Y., Mattmann, C., and Wilson, B. (2020). ConvCast: An Embedded Convolutional LSTM Based Architecture for Precipitation Nowcasting Using Satellite Data. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0230114"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1038\/s41586-021-03854-z","article-title":"Skilful Precipitation Nowcasting Using Deep Generative Models of Radar","volume":"597","author":"Ravuri","year":"2021","journal-title":"Nature"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"111434","DOI":"10.1016\/j.engappai.2025.111434","article-title":"Deep Learning and Adaptive Boosting for Hydroelectric Power Prediction Using Hydro-Meteorological Data: Insights and Feature Importance Analysis","volume":"158","author":"Karakoyun","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2633","DOI":"10.1007\/s11600-023-01150-0","article-title":"Chaotic Analysis of Daily Runoff Time Series Using Dynamic, Metric, and Topological Approaches","volume":"72","author":"Benmebarek","year":"2024","journal-title":"Acta Geophys."},{"key":"ref_34","unstructured":"(2025, January 30). Weather Radar Archive, Available online: https:\/\/meteo.arso.gov.si\/."},{"key":"ref_35","unstructured":"(2025, January 30). Electricity Data Repository. Available online: https:\/\/mojelektro.si\/login."},{"key":"ref_36","first-page":"2825","article-title":"Scikit-Learn: Machine Learning in {P}ython","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_37","first-page":"61","article-title":"Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods","volume":"10","author":"Platt","year":"1999","journal-title":"Advances in Large Margin Classifiers"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","article-title":"Nearest Neighbor Pattern Classification","volume":"13","author":"Cover","year":"1967","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Niculescu-Mizil, A., and Caruana, R. (2005, January 7\u201311). Predicting Good Probabilities with Supervised Learning. Proceedings of the 22nd International Conference on Machine Learning (ICML), Bonn, Germany.","DOI":"10.1145\/1102351.1102430"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"130948","DOI":"10.1016\/j.physa.2025.130948","article-title":"Time Series Forecasting Enhanced by Lyapunov Exponent via Attention Mechanism","volume":"678","author":"Lima","year":"2025","journal-title":"Phys. A Stat. Mech. Its Appl."},{"key":"ref_41","first-page":"2540055","article-title":"Integrating Chaotic Analysis with Dual Deep Learning Models for Accurate Wind Speed Forecasting. Energy Sources Part A Recover","volume":"47","author":"Ahuja","year":"2025","journal-title":"Util. Environ. Eff."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wang, F., Fan, Z., Fan, Y., Ren, J., Li, Y., Suo, L., and Tang, J. (2025). Research on Energy Storage Configuration Optimization Method for Wind Farm Substations Based on Wind Power Fluctuation Prediction Integrating Chaotic Features and Bidirectional Gated Recurrent Units. Algorithms, 18.","DOI":"10.3390\/a18110698"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"127901","DOI":"10.1016\/j.jhydrol.2022.127901","article-title":"Rainfall-Runoff Modeling Using Long Short-Term Memory Based Step-Sequence Framework","volume":"610","author":"Yin","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhang, G., Li, H., Wang, L., Wang, W., Guo, J., Qin, H., and Ni, X. (2024). Research on Medium- and Long-Term Hydropower Generation Forecasting Method Based on LSTM and Transformer. Energies, 17.","DOI":"10.3390\/en17225707"}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/7\/4\/163\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T15:56:39Z","timestamp":1765295799000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/7\/4\/163"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,9]]},"references-count":44,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["make7040163"],"URL":"https:\/\/doi.org\/10.3390\/make7040163","relation":{},"ISSN":["2504-4990"],"issn-type":[{"value":"2504-4990","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,9]]}}}