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26 Nov 2012
A hybrid model of self organizing maps and least square support vector machine for river flow forecasting
S. Ismail, A. Shabri, and R. Samsudin
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Total article views: 4,420 (including HTML, PDF, and XML)
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Total |
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| 1,696 |
2,316 |
408 |
4,420 |
151 |
116 |
- HTML: 1,696
- PDF: 2,316
- XML: 408
- Total: 4,420
- BibTeX: 151
- EndNote: 116
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Cumulative views and downloads
(calculated since 01 Feb 2013, article published on 18 Oct 2010)
Total article views: 3,225 (including HTML, PDF, and XML)
| HTML |
PDF |
XML |
Total |
BibTeX |
EndNote |
| 1,329 |
1,516 |
380 |
3,225 |
137 |
106 |
- HTML: 1,329
- PDF: 1,516
- XML: 380
- Total: 3,225
- BibTeX: 137
- EndNote: 106
Views and downloads (calculated since 01 Feb 2013, article published on 26 Nov 2012)
Cumulative views and downloads
(calculated since 01 Feb 2013, article published on 26 Nov 2012)
Total article views: 1,195 (including HTML, PDF, and XML)
| HTML |
PDF |
XML |
Total |
BibTeX |
EndNote |
| 367 |
800 |
28 |
1,195 |
14 |
10 |
- HTML: 367
- PDF: 800
- XML: 28
- Total: 1,195
- BibTeX: 14
- EndNote: 10
Views and downloads (calculated since 01 Feb 2013, article published on 18 Oct 2010)
Cumulative views and downloads
(calculated since 01 Feb 2013, article published on 18 Oct 2010)
Cited
28 citations as recorded by crossref.
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25 citations as recorded by crossref.
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Comment on "A hybrid model of self organizing maps and least square support vector machine for river flow forecasting" by Ismail et al. (2012)
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10.5194/hess-18-2711-2014
-
Univariate streamflow forecasting using commonly used data-driven models: literature review and case study
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A novel prediction model of PM2.5 mass concentration based on back propagation neural network algorithm
Y. Chen et al.
10.3233/JIFS-179119
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The Application of a Hybrid Model Using Mathematical Optimization and Intelligent Algorithms for Improving the Talc Pellet Manufacturing Process
D. Buntam et al.
10.3390/sym12101602
-
Using self-organizing maps to infill missing data in hydro-meteorological time series from the Logone catchment, Lake Chad basin
E. Nkiaka et al.
10.1007/s10661-016-5385-1
-
The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management
V. Kumar et al.
10.3390/su151310543
-
A New Approach for Modeling Sediment-Discharge Relationship: Local Weighted Linear Regression
O. Kisi & C. Ozkan
10.1007/s11269-016-1481-9
-
Determination of Surface and Hole Quality in Drilling of AISI D2 Cold Work Tool Steel Using MPMR, MARS and LSSVM
P. Samui
10.1142/S0219686714500140
-
NOVEL APPROACH TO IMPROVE GEOCENTRIC TRANSLATION MODEL PERFORMANCE USING ARTIFICIAL NEURAL NETWORK TECHNOLOGY
Y. Ziggah et al.
10.1590/s1982-21702017000100014
-
Machine learning and regression-based techniques for predicting sprinkler irrigation's wind drift and evaporation losses
M. Mattar et al.
10.1016/j.agwat.2022.107529
-
SOM-Based Decision Support System for Reservoir Operation Management
R. Rodríguez-Alarcón & S. Lozano
10.1061/(ASCE)HE.1943-5584.0001496
-
A hybrid forecasting model based on the group method of data handling and wavelet decomposition for monthly rivers streamflow data sets
W. Shaikh et al.
10.1515/phys-2022-0066
-
Modeling of daily pan evaporation in sub tropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS
M. Goyal et al.
10.1016/j.eswa.2014.02.047
-
Development of a coupled wavelet transform and evolutionary Levenberg‐Marquardt neural networks for hydrological process modeling
P. Abbaszadeh et al.
10.1111/coin.12124
-
Monthly Streamflow Modeling Based on Self-Organizing Maps and Satellite-Estimated Rainfall Data
T. do Nascimento et al.
10.1007/s11269-022-03147-8
-
Characterization of soil moisture response patterns and hillslope hydrological processes through a self-organizing map
E. Lee & S. Kim
10.5194/hess-25-5733-2021
-
Wavelet Decomposition Impacts on Traditional Forecasting Time Series Models
W. A. Shaikh et al.
10.32604/cmes.2022.017822
-
Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey Wolf optimization (GWO) algorithm
Y. Tikhamarine et al.
10.1016/j.jhydrol.2019.124435
-
Capability of Artificial Neural Network for Forward Conversion of Geodetic Coordinates $$(\phi ,\lambda ,h)$$ ( ϕ , λ , h ) to Cartesian Coordinates (X, Y, Z)
Y. Ziggah et al.
10.1007/s11004-016-9638-x
-
A Novel LSSVM Based Algorithm to Increase Accuracy of Bacterial Growth Modeling
M. Salehi Borujeni et al.
10.21859/ijb.1542
-
Self-organizing maps by difference of convex functions optimization
H. Le Thi & M. Nguyen
10.1007/s10618-014-0369-7
-
The municipal solid waste generation distribution prediction system based on FIG–GA-SVR model
F. Dai et al.
10.1007/s10163-020-01022-5
-
Towards Safer Data-Driven Forecasting of Extreme Streamflows
J. Matos et al.
10.1007/s11269-017-1834-z
-
Reply to the “Discussion by Haddad et al. on ‘Hydroclimatic stream flow prediction using least square-support vector regression’ by Bhagwat and Maity (2013)”
P. Bhagwat & R. Maity
10.1080/09715010.2014.881075
-
River Stage Forecasting Using Wavelet Packet Decomposition and Machine Learning Models
Y. Seo et al.
10.1007/s11269-016-1409-4
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