scholarly journals River Flow Forecasting: a Hybrid Model of Self Organizing Maps and Least Square Support Vector Machine

2010 ◽  
Vol 7 (5) ◽  
pp. 8179-8212 ◽  
Author(s):  
S. Ismail ◽  
R. Samsudin ◽  
A. Shabri
Keyword(s):  
Support Vector Machine ◽  
Hybrid Model ◽  
River Flow ◽  
Linear Method ◽  
Least Square ◽  
Support Vector ◽  
Promising Alternative ◽  
River Flow Forecasting ◽  
Proposed Model ◽  
Self Organizing

Abstract. Successful river flow time series forecasting is a major goal and an essential procedure that is necessary in water resources planning and management. This study introduced a new hybrid model based on a combination of two familiar non-linear method of mathematical modeling: Self Organizing Map (SOM) and Least Square Support Vector Machine (LSSVM) model referred as SOM-LSSVM model. The hybrid model uses the SOM algorithm to cluster the training data into several disjointed clusters and the individual LSSVM is used to forecast the river flow. The feasibility of this proposed model is evaluated to actual river flow data from Bernam River located in Selangor, Malaysia. Their results have been compared to those obtained using LSSVM and artificial neural networks (ANN) models. The experiment results show that the SOM-LSSVM model outperforms other models for forecasting river flow. It also indicates that the proposed model can forecast more precisely and provides a promising alternative technique in river flow forecasting.

scholarly journals A hybrid model of self organizing maps and least square support vector machine for river flow forecasting

2012 ◽  
Vol 16 (11) ◽  
pp. 4417-4433 ◽  
Author(s):  
S. Ismail ◽  
A. Shabri ◽  
R. Samsudin
Keyword(s):  
Support Vector Machine ◽  
Hybrid Model ◽  
River Flow ◽  
Resource Planning ◽  
Support Vector ◽  
Promising Alternative ◽  
Output Layer ◽  
River Flow Forecasting ◽  
Proposed Model ◽  
Self Organizing

Abstract. Successful river flow forecasting is a major goal and an essential procedure that is necessary in water resource planning and management. There are many forecasting techniques used for river flow forecasting. This study proposed a hybrid model based on a combination of two methods: Self Organizing Map (SOM) and Least Squares Support Vector Machine (LSSVM) model, referred to as the SOM-LSSVM model for river flow forecasting. The hybrid model uses the SOM algorithm to cluster the entire dataset into several disjointed clusters, where the monthly river flows data with similar input pattern are grouped together from a high dimensional input space onto a low dimensional output layer. By doing this, the data with similar input patterns will be mapped to neighbouring neurons in the SOM's output layer. After the dataset has been decomposed into several disjointed clusters, an individual LSSVM is applied to forecast the river flow. The feasibility of this proposed model is evaluated with respect to the actual river flow data from the Bernam River located in Selangor, Malaysia. The performance of the SOM-LSSVM was compared with other single models such as ARIMA, ANN and LSSVM. The performance of these models was then evaluated using various performance indicators. The experimental results show that the SOM-LSSVM model outperforms the other models and performs better than ANN, LSSVM as well as ARIMA for river flow forecasting. It also indicates that the proposed model can forecast more precisely, and provides a promising alternative technique for river flow forecasting.

scholarly journals Comment on "A hybrid model of self organizing maps and least square support vector machine for river flow forecasting" by Ismail et al. (2012)

2014 ◽  
Vol 18 (7) ◽  
pp. 2711-2714 ◽  
Author(s):  
F. Fahimi ◽  
A. H. El-Shafie
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Water Resource Management ◽  
River Flow ◽  
Moving Average ◽  
Least Square ◽  
Support Vector ◽  
Self Organizing Map ◽  
River Flow Forecasting ◽  
Self Organizing

Abstract. Without a doubt, river flow forecasting is one of the most important issues in water engineering field. There are lots of forecasting techniques that have successfully been utilized by previously conducted studies in water resource management and water engineering. The study of Ismail et al. (2012), which was published in the journal Hydrology and Earth System Sciences in 2012, was a valuable piece of research that investigated the combination of two effective methods (self-organizing map and least squares support vector machine) for river flow forecasting. The goal was to make a comparison between the performances of self organizing map and least square support vector machine (SOM-LSSVM), autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and least squares support vector machine (LSSVM) models for river flow prediction. This comment attempts to focus on some parts of the original paper that need more discussion. The emphasis here is to provide more information about the accuracy of the observed river flow data and the optimum map size for SOM mode as well.

scholarly journals Comment on "A hybrid model of self organizing maps and least square support vector machine for river flow forecasting" by Ismail et al. (2012)

2013 ◽  
Vol 10 (11) ◽  
pp. 13889-13895
Author(s):  
F. Fahimi ◽  
A. H. El-Shafie
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Water Resource Management ◽  
River Flow ◽  
Moving Average ◽  
Least Square ◽  
Support Vector ◽  
Self Organizing Map ◽  
River Flow Forecasting ◽  
Self Organizing

Abstract. Without a doubt, river flow forecasting is one of the most important issues in water engineering field. There are lots of forecasting techniques, which have successfully been utilized by previously conducted studies in water resource management and water engineering. The study of Ismail et al. (2012) which has been published in Journal of Hydrology and Earth System Sciences in 2012 was a valuable research that investigated the combination of two effective methods (self-organizing map and least squares support vector machine) for river flow forecasting. The goal was to make a comparison between the performances of SOM-LSSVM, autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and least squares support vector machine (LSSVM) models for river flow prediction. This comment attempts to focus on some parts of the original paper that need more discussion. The emphasis here is to provide more information about the accuracy of the observed river flow data and the optimum map size for SOM mode as well.

A COMPARITIVE ANALYSIS AND TIME SERIES FORECASTING OF MONTHLY STREAM FLOW DATA USING HYBRID MODEL

2015 ◽  
Vol 76 (13) ◽  
Author(s):  
Siraj Muhammed Pandhiani ◽  
Ani Shabri
Keyword(s):  
Hybrid Model ◽  
Stream Flow ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Least Square ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Flow Data ◽  
Indus River ◽  
Proposed Model

In this study, new hybrid model is developed by integrating two models, the discrete wavelet transform and least square support vector machine (WLSSVM) model. The hybrid model is then used to measure for monthly stream flow forecasting for two major rivers in Pakistan. The monthly stream flow forecasting results are obtained by applying this model individually to forecast the rivers flow data of the Indus River and Neelum Rivers. The root mean square error (RMSE), mean absolute error (MAE) and the correlation (R) statistics are used for evaluating the accuracy of the WLSSVM, the proposed model. The results are compared with the results obtained through LSSVM. The outcome of such comparison shows that WLSSVM model is more accurate and efficient than LSSVM.

Deep Learning in Engineering Education

2020 ◽  
pp. 187-218
Author(s):  
Deepali R. Vora ◽  
Kamatchi R. Iyer
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Deep Learning ◽  
Information Systems ◽  
Engineering Education ◽  
Hybrid Model ◽  
Cuckoo Search ◽  
Support Vector ◽  
Proposed Model ◽  
Class Labels

The goodness measure of any institute lies in minimising the dropouts and targeting good placements. So, predicting students' performance is very interesting and an important task for educational information systems. Machine learning and deep learning are the emerging areas that truly entice more research practices. This research focuses on applying the deep learning methods to educational data for classification and prediction. The educational data of students from engineering domain with cognitive and non-cognitive parameters is considered. The hybrid model with support vector machine (SVM) and deep belief network (DBN) is devised. The SVM predicts class labels from preprocessed data. These class labels and actual class labels acts as input to the DBN to perform final classification. The hybrid model is further optimised using cuckoo search with Levy flight. The results clearly show that the proposed model SVM-LCDBN gives better performance as compared to simple hybrid model and hybrid model with traditional cuckoo search.

scholarly journals Icing Forecasting of High Voltage Transmission Line Using Weighted Least Square Support Vector Machine with Fireworks Algorithm for Feature Selection

2016 ◽  
Author(s):  
Tiannan Ma ◽  
Dongxiao Niu
Keyword(s):  
Support Vector Machine ◽  
Least Square ◽  
Support Vector ◽  
Data Set ◽  
Promising Alternative ◽  
Forecasting Accuracy ◽  
High Voltage Transmission Line ◽  
Fireworks Algorithm ◽  
Weighted Least Square ◽  
Forecasting System

Accurate forecasting of icing thickness has a great significance for ensuring the security and stability of power grid. In order to improve the forecasting accuracy, this paper proposes an icing forecasting system based on fireworks algorithm and weighted least square support vector machine (W-LSSVM). The method of fireworks algorithm is employed to select the proper input features with the purpose of eliminating the redundant influence. In addition, the aim of W-LSSVM model is to train and test the historical data-set with the selected features. The capability of this proposed icing forecasting model and framework is tested through the simulation experiments using real-world icing data from monitoring center of key laboratory of anti-ice disaster, Hunan, South China. The results show that the proposed W-LSSVM-FA method has a higher prediction accuracy and it may be a promising alternative for icing thickness forecasting.

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