Evidence map›Paper›PMID 40707513›Full record

ArticleScientific reports2025

Prediction of the monthly river water level by using ensemble decomposition modeling.

Chaitanya Baliram Pande, Lariyah Mohd Sidek, Bijay Halder, Okan Mert Katipoğlu, Jitendra Rajput, Fahad Alshehri, Rabin Chakrabortty, Subodh Chandra Pal, Norlida Mohd Dom, Miklas Scholz

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Chaitanya Baliram PandeInstitute of Energy Infrastructure, Universiti Tenaga Nasional, 43000, Kajang, Malaysia. chaitanay45@gmail.com.
Lariyah Mohd SidekInstitute of Energy Infrastructure, Universiti Tenaga Nasional, 43000, Kajang, Malaysia.
Bijay HalderDepartment of Earth Sciences and Environment, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia.
Okan Mert KatipoğluFaculty of Engineering and Architecture, Department of Civil Engineering, Erzincan Binali Yıldırım University, 24100, Erzincan, Turkey.
Jitendra RajputDivision of Agricultural Engineering, ICAR-Indian Agricultural Research Institute, New Delhi-110012, India.
Fahad AlshehriAbdullah Alrushaid Chair for Earth Science Remote Sensing Research, Geology and Geophysics Department, King Saud University, Riyadh, Saudi Arabia.
Rabin ChakraborttyDepartment of Civil Engineering, American University of Sharjah, 26666, Sharjah, United Arab Emirates.
Subodh Chandra PalDepartment of Geography, The University of Burdwan, Purba Bardhaman, Burdwan, West Bengal, 713104, India.
Norlida Mohd DomDrainage & Irrigation Department, Kuala Lumpur, Selangor, Malaysia.
Miklas ScholzDepartment of Civil Engineering Science, School of Civil Engineering and the Built Environment, Faculty of Engineering and the Built Environment, University of Johannesburg, Kingsway Campus, Aukland Park, P.O. Box 524, Johannesburg, 2006, South Africa. mscholz@uj.ac.za.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The decomposition, artificial intelligence (AI) and machine learning (ML) modeling have been important role in hydrological and river basin related prediction and forecasting to help the flood management and sustainable water resources development. In this paper, developed the hybrid modeling combined with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), along with standalone models support vector machine (SVM-linear), and Random Forest (RF), Random Subspace (RS) for accurate prediction of monthly river water level in Sg Muar at Buloh Kasap, Johor station during 2014 to 2023. In this paper, two combinations of variables such as Lags and IMFS are used for development of different models for river water level prediction. Hence, these models are compared and measured the performance of models based on the various statistics metrics. Therefore hybrid models performance is measured based on the coefficient of determination (R

Indexed as

Artificial intelligenceData decompositionEnergyMuar riverPerformance assessmentRiver water levelSustainable development goal

Identifiers

PMID40707513
PMCPMC12290084

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.