Title: FLEXIBLE AND OPTIMAL M5 MODEL TREES WITH APPLICATIONS TO FLOW PREDICTIONS
Abstract: Hydroinformatics, pp. 1719-1726 (2004) No AccessFLEXIBLE AND OPTIMAL M5 MODEL TREES WITH APPLICATIONS TO FLOW PREDICTIONSDIMITRI P. SOLOMATINE and MICHAEL BASKARA L. A. SIEKDIMITRI P. SOLOMATINEUNESCO-IHE Institute for Water Education, P.O. Box 3015 Delft, The Netherlands and MICHAEL BASKARA L. A. SIEKUNESCO-IHE Institute for Water Education, P.O. Box 3015 Delft, The Netherlandshttps://doi.org/10.1142/9789812702838_0212Cited by:12 PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Abstract: M5 is a method developed by Quinlan [10] for inducing trees of linear regression models (model trees). The paper addresses the flexibility and optimality in M5 model tree by proposing two new algorithms, namely M5flex and M5opt. M5flex algorithm brings in domain knowledge by enabling the user to choose split attributes and split values for important nodes in a model tree so that the resulting model would be more accurate, reliable and appropriate for practical applications. M5opt is a semi-non-greedy algorithm with a number of improvements if compared with M5. For experiments six hydrological data sets and five benchmark data sets were used. For comparison, M5' and ANN algorithms were employed as well. Overall, M5flex was the most accurate, followed by M5opt, M5'and ANN. FiguresReferencesRelatedDetailsCited By 12Deep neural network based pier scour modelingMahesh Pal16 October 2019 | ISH Journal of Hydraulic Engineering, Vol. 28, No. sup1Comparison of Numerical and Data Driven Approaches for Rainfall-Runoff ModelingDigvijay Saruk, Shreenivas Londhe, Pradnya Dixit and Preeti Kulkarni4 August 2022Application of stacking hybrid machine learning algorithms in delineating multi-type flooding in BangladeshMahfuzur Rahman, Ningsheng Chen, Ahmed Elbeltagi, Md Monirul Islam and Mehtab Alam et al.1 Oct 2021 | Journal of Environmental Management, Vol. 295Applying several machine learning approaches for prediction of unconfined compressive strength of stabilized pond ashesManju Suthar8 August 2019 | Neural Computing and Applications, Vol. 32, No. 13Intermittent Reservoir Daily Inflow Prediction Using Stochastic and Model Tree TechniquesDeepali More, R. B. Magar and V. Jothiprakash9 March 2019 | Journal of The Institution of Engineers (India): Series A, Vol. 100, No. 3Prediction of cumulative infiltration of sandy soil using random forest approachParveen Sihag, N. K. Tiwari and Subodh Ranjan16 July 2018 | Journal of Applied Water Engineering and Research, Vol. 7, No. 2Groundwater Level Prediction using M5 Model TreesNitha Ayinippully Nalarajan and C. Mohandas8 January 2015 | Journal of The Institution of Engineers (India): Series A, Vol. 96, No. 1Comparison of M5' Model Tree with MLR in the Development of Fault Prediction Models Involving Interaction Between MetricsRinkaj Goyal, Pravin Chandra and Yogesh Singh8 November 2014Determining Flow Friction Factor in Irrigation Pipes Using Data Mining and Artificial Intelligence ApproachesSaeed Samadianfard, Mohammad Taghi Sattari, Ozgur Kisi and Honeyeh Kazemi7 October 2014 | Applied Artificial Intelligence, Vol. 28, No. 8Prediction of regional streamflow frequency using model tree ensemblesSpencer Schnier and Ximing Cai1 Sep 2014 | Journal of Hydrology, Vol. 517Real time wave forecasting using wind time history and numerical modelPooja Jain, M.C. Deo, G. Latha and V. Rajendran1 Jan 2011 | Ocean Modelling, Vol. 36, No. 1-2Data-Driven Modeling and Computational Intelligence Methods in HydrologyDimitri P Solomatine15 April 2006 HydroinformaticsMetrics History PDF download
Publication Year: 2004
Publication Date: 2004-06-01
Language: en
Type: book-chapter
Indexed In: ['crossref']
Access and Citation
Cited By Count: 35
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