PMID- 34023993
OWN - NLM
STAT- MEDLINE
DCOM- 20210929
LR  - 20210929
IS  - 1614-7499 (Electronic)
IS  - 0944-1344 (Linking)
VI  - 28
IP  - 38
DP  - 2021 Oct
TI  - Regression models for sediment transport in tropical rivers.
PG  - 53097-53115
LID - 10.1007/s11356-021-14479-0 [doi]
AB  - The investigation of sediment transport in tropical rivers is essential for 
      planning effective integrated river basin management to predict the changes in 
      rivers. The characteristics of rivers and sediment in the tropical region are 
      different compared to those of the rivers in Europe and the USA, where the median 
      sediment size tends to be much more refined. The origins of the rivers are mainly 
      tropical forests. Due to the complexity of determining sediment transport, many 
      sediment transport equations were recommended in the literature. However, the 
      accuracy of the prediction results remains low, particularly for the tropical 
      rivers. The majority of the existing equations were developed using multiple 
      non-linear regression (MNLR). Machine learning has recently been the method of 
      choice to increase model prediction accuracy in complex hydrological problems. 
      Compared to the conventional MNLR method, machine learning algorithms have 
      advanced and can produce a useful prediction model. In this research, three 
      machine learning models, namely evolutionary polynomial regression (EPR), 
      multi-gene genetic programming (MGGP) and M5 tree model (M5P), were implemented 
      to model sediment transport for rivers in Malaysia. The formulated variables for 
      the prediction model were originated from the revised equations reported in the 
      relevant literature for Malaysian rivers. Among the three machine learning 
      models, in terms of different statistical measurement criteria, EPR gives the 
      best prediction model, followed by MGGP and M5P. Machine learning is excellent at 
      improving the prediction distribution of high data values but lacks accuracy 
      compared to observations of lower data values. These results indicate that 
      further study needs to be done to improve the machine learning model's accuracy 
      to predict sediment transport.
CI  - (c) 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Harun, Mohd Afiq
AU  - Harun MA
AD  - River Engineering and Urban Drainage Research Centre (REDAC), Universiti Sains 
      Malaysia, Engineering Campus, 14300, Nibong Tebal, Penang, Malaysia.
FAU - Safari, Mir Jafar Sadegh
AU  - Safari MJS
AUID- ORCID: 0000-0003-0559-5261
AD  - Department of Civil Engineering, Yasar University, Izmir, Turkey. 
      jafar.safari@yasar.edu.tr.
FAU - Gul, Enes
AU  - Gul E
AD  - Department of Civil Engineering, Inonu University, Malatya, Turkey.
FAU - Ab Ghani, Aminuddin
AU  - Ab Ghani A
AD  - River Engineering and Urban Drainage Research Centre (REDAC), Universiti Sains 
      Malaysia, Engineering Campus, 14300, Nibong Tebal, Penang, Malaysia.
LA  - eng
PT  - Journal Article
DEP - 20210522
PL  - Germany
TA  - Environ Sci Pollut Res Int
JT  - Environmental science and pollution research international
JID - 9441769
SB  - IM
MH  - Algorithms
MH  - *Geologic Sediments
MH  - Machine Learning
MH  - Regression Analysis
MH  - *Rivers
OTO - NOTNLM
OT  - Machine learning
OT  - Malaysia rivers
OT  - Sediment transport
OT  - Total bed material load
OT  - Tropical rivers
EDAT- 2021/05/24 06:00
MHDA- 2021/09/30 06:00
CRDT- 2021/05/23 21:04
PHST- 2021/02/15 00:00 [received]
PHST- 2021/05/14 00:00 [accepted]
PHST- 2021/05/24 06:00 [pubmed]
PHST- 2021/09/30 06:00 [medline]
PHST- 2021/05/23 21:04 [entrez]
AID - 10.1007/s11356-021-14479-0 [pii]
AID - 10.1007/s11356-021-14479-0 [doi]
PST - ppublish
SO  - Environ Sci Pollut Res Int. 2021 Oct;28(38):53097-53115. doi: 
      10.1007/s11356-021-14479-0. Epub 2021 May 22.