A Machine Learning Framework to Predict Reverse Flow and Water Level: A Case Study of Tonle Sap Lake

Journal of Hydrology ◽

10.1016/j.jhydrol.2021.127168 ◽

2021 ◽

pp. 127168

Author(s):

Khosro Morovati ◽

Pouria Nakhaei ◽

Fuqiang Tian ◽

Mahmut Tudaji ◽

Shiyu Hou

Keyword(s):

Machine Learning ◽

Water Level ◽

Reverse Flow ◽

Learning Framework ◽

Tonle Sap Lake ◽

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A hybrid machine learning framework for real-time water level prediction in high sediment load reaches

Journal of Hydrology ◽

10.1016/j.jhydrol.2019.124422 ◽

2020 ◽

Vol 581 ◽

pp. 124422 ◽

Author(s):

Gang Zhao ◽

Bo Pang ◽

Zongxue Xu ◽

Liyang Xu

Keyword(s):

Machine Learning ◽

Water Level ◽

Real Time ◽

Sediment Load ◽

Learning Framework ◽

Hybrid Machine ◽

High Sediment ◽

Water Level Prediction

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Hindcast and forecast of daily inundation extents using satellite SAR and altimetry data with rotated empirical orthogonal function analysis: Case study in Tonle Sap Lake Floodplain

Remote Sensing of Environment ◽

10.1016/j.rse.2020.111732 ◽

2020 ◽

Vol 241 ◽

pp. 111732 ◽

Author(s):

Chi-Hung Chang ◽

Hyongki Lee ◽

Donghwan Kim ◽

Euiho Hwang ◽

Faisal Hossain ◽

...

Keyword(s):

Empirical Orthogonal Function ◽

Function Analysis ◽

Empirical Orthogonal Function Analysis ◽

Altimetry Data ◽

Orthogonal Function ◽

Tonle Sap Lake ◽

Tonle Sap ◽

Rotated Empirical Orthogonal Function

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Wrapping practical problems into a machine learning framework: using water pipe failure prediction as a case study

International Journal of Intelligent Systems Technologies and Applications ◽

10.1504/ijista.2017.10005998 ◽

2017 ◽

Vol 16 (3) ◽

pp. 191

Author(s):

Fang Chen ◽

Jianlong Zhou ◽

Jinjun Sun ◽

Yang Wang

Keyword(s):

Machine Learning ◽

Failure Prediction ◽

Pipe Failure ◽

Learning Framework

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CAN MACHINE LEARNING IMPROVE THE ACCURACY OF WATER LEVEL FORECASTS FOR INLAND NAVIGATION? CASE STUDY: RHINE RIVER BASIN, GERMANY

10.3850/38wc092019-0274 ◽

2019 ◽

Author(s):

YUELING MA ◽

ELENA MATTA ◽

DENNIS MEIßNER ◽

HANNO SCHELLENBERG ◽

REINHARD HINKELMANN

Keyword(s):

Machine Learning ◽

Water Level ◽

River Basin ◽

Rhine River ◽

Inland Navigation

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A General Machine Learning Framework for Solving Real Applications with a Texture Perception Case Study

10.1109/icivc52351.2021.9526959 ◽

2021 ◽

Author(s):

Qin Zhang ◽

Jing Liu ◽

Huali Sun ◽

Jun Liu

Keyword(s):

Machine Learning ◽

Texture Perception ◽

Learning Framework

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A machine learning framework with dataset-knowledgeability pre-assessment and a local decision-boundary crispness score: An industry 4.0-based case study on composite autoclave manufacturing

Computers in Industry ◽

10.1016/j.compind.2021.103510 ◽

2021 ◽

Vol 132 ◽

pp. 103510

Author(s):

Bryn Crawford ◽

Reza Sourki ◽

Hamid Khayyam ◽

Abbas S. Milani

Keyword(s):

Machine Learning ◽

Industry 4.0 ◽

Decision Boundary ◽

Learning Framework ◽

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Impact of Water Level Fluctuation on Sediment and Phosphorous Dynamics in Tonle Sap Lake, Cambodia

Water Air & Soil Pollution ◽

10.1007/s11270-021-05084-5 ◽

2021 ◽

Vol 232 (4) ◽

Author(s):

Rajendra Khanal ◽

Sovannara Uk ◽

Dilini Kodikara ◽

Sokly Siev ◽

Chihiro Yoshimura

Keyword(s):

Water Level ◽

Water Level Fluctuation ◽

Level Fluctuation ◽

Tonle Sap Lake ◽

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Wrapping practical problems into a machine learning framework: using water pipe failure prediction as a case study

International Journal of Intelligent Systems Technologies and Applications ◽

10.1504/ijista.2017.085355 ◽

2017 ◽

Vol 16 (3) ◽

pp. 191 ◽

Author(s):

Jianlong Zhou ◽

Jinjun Sun ◽

Yang Wang ◽

Fang Chen

Keyword(s):

Machine Learning ◽

Failure Prediction ◽

Pipe Failure ◽

Learning Framework

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Assessment of Satellite Rainfall Estimates as a Pre-Analysis for Water Environment Analytical Tools: A Case Study for Tonle Sap Lake in Cambodia

Engineering Journal ◽

10.4186/ej.2018.22.1.229 ◽

2018 ◽

Vol 22 (1) ◽

pp. 229-241 ◽

Author(s):

Chanarun Phoeurn ◽

Sarann Ly

Keyword(s):

Water Environment ◽

Tonle Sap Lake ◽

Tonle Sap ◽

Satellite Rainfall ◽

Analytical Tools ◽

Rainfall Estimates

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A Strategy for Fisheries Resources Management in Southeast Asia: A Case Study of an Inland Fishery around Tonle Sap Lake in Cambodia

Aqua-BioScience Monographs ◽

10.5047/absm.2017.01002.0023 ◽

2017 ◽

Vol 10 (2) ◽

pp. 23-40 ◽

Author(s):

Satoshi Ishikawa ◽

Mina Hori ◽

Hisashi Kurokura

Keyword(s):

Southeast Asia ◽

Resources Management ◽

Fisheries Resources ◽

Tonle Sap Lake ◽

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