{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T13:39:33Z","timestamp":1777729173638,"version":"3.51.4"},"reference-count":90,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,7,31]],"date-time":"2020-07-31T00:00:00Z","timestamp":1596153600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"university of Kurdistan, Iran \u200e","award":["GRC98-04469-1\u200e"],"award-info":[{"award-number":["GRC98-04469-1\u200e"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Zrebar Lake is one of the largest freshwater lakes in Iran and it plays an important role in the ecosystem of the environment, while its desiccation has a negative impact on the surrounded ecosystem. Despite this, this lake provides an interesting recreation setting in terms of ecotourism. The prediction and forecasting of the water level of the lake through simple but practical methods can provide a reliable tool for future lake water resource management. In the present study, we predict the daily water level of Zrebar Lake in Iran through well-known decision tree-based algorithms, including the M5 pruned (M5P), random forest (RF), random tree (RT) and reduced error pruning tree (REPT). We used five different water input combinations to find the most effective one. For our modeling, we chose 70% of the dataset for training (from 2011 to 2015) and 30% for model evaluation (from 2015 to 2017). We evaluated the models\u2019 performances using different quantitative (root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), percent bias (PBIAS) and ratio of the root mean square error to the standard deviation of measured data (RSR)) and visual frameworks (Taylor diagram and box plot). Our results showed that water level with a one-day lag time had the highest effect on the result and, by increasing the lag time, its effect on the result was decreased. This result indicated that all the developed models had a good prediction capability, but the M5P model outperformed the others, followed by RF and RT equally and then REPT. Our results showed that these algorithms can predict water level accurately only with a one-day lag time in water level as an input and they are cost-effective tools for future predictions.<\/jats:p>","DOI":"10.3390\/ijgi9080479","type":"journal-article","created":{"date-parts":[[2020,7,31]],"date-time":"2020-07-31T04:15:31Z","timestamp":1596168931000},"page":"479","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":72,"title":["Daily Water Level Prediction of Zrebar Lake (Iran): A Comparison between M5P, Random Forest, Random Tree and Reduced Error Pruning Trees Algorithms"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1812-950X","authenticated-orcid":false,"given":"Viet-Ha","family":"Nhu","sequence":"first","affiliation":[{"name":"Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam"},{"name":"Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5091-6947","authenticated-orcid":false,"given":"Himan","family":"Shahabi","sequence":"additional","affiliation":[{"name":"Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran"},{"name":"Board Member of Department of Zrebar Lake Environmental Research, Kurdistan Studies Institute, University of Kurdistan, Sanandaj 66177-15175, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ebrahim","family":"Nohani","sequence":"additional","affiliation":[{"name":"Young Researchers and Elite Club, Dezful Branch, Islamic Azad University, Dezful 64616-45169, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9668-8687","authenticated-orcid":false,"given":"Ataollah","family":"Shirzadi","sequence":"additional","affiliation":[{"name":"Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6790-2653","authenticated-orcid":false,"given":"Nadhir","family":"Al-Ansari","sequence":"additional","affiliation":[{"name":"Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 971 87 Lulea, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sepideh","family":"Bahrami","sequence":"additional","affiliation":[{"name":"Department of Hydrological Sciences, University of Nevada, 89557-02601-775-685-8040, Reno, NV 89557, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaghayegh","family":"Miraki","sequence":"additional","affiliation":[{"name":"Department of Watershed Sciences Engineering, Faculty of Natural Resources, University of Agricultural Science and Natural Resources of Sari, Mazandaran 48181-68984, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marten","family":"Geertsema","sequence":"additional","affiliation":[{"name":"Natural Resource Operations and Rural Development, Ministry of Forests, Lands, Prince George, BC V2L 1R5, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6122-8314","authenticated-orcid":false,"given":"Hoang","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,31]]},"reference":[{"key":"ref_1","unstructured":"Vuglinskiy, V. 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