Enforced self‐organizing map neural networks for river flood forecasting

Chang, Fi‐John; Chang, Li-Chiu; Wang, Yan-Shiang

doi:10.1002/hyp.6262

Cited by 52 publications

(19 citation statements)

References 27 publications

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“…The input layer is composed of n input nodes, each connected to all nodes of the Kohonen layer [50][51][52][53][54]. The Kohonen layer consists of [n1-by-n1] matrices.…”

Section: Kohonen Self-organizing Feature Map (Ksofm) Modelmentioning

confidence: 99%

Spatial Disaggregation of Areal Rainfall Using Two Different Artificial Neural Networks Models

Kim

Singh

2015

Water

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Abstract:The objective of this study is to develop artificial neural network (ANN) models, including multilayer perceptron (MLP) and Kohonen self-organizing feature map (KSOFM), for spatial disaggregation of areal rainfall in the Wi-stream catchment, an International Hydrological Program (IHP) representative catchment, in South Korea. A three-layer MLP model, using three training algorithms, was used to estimate areal rainfall. The Levenberg-Marquardt training algorithm was found to be more sensitive to the number of hidden nodes than were the conjugate gradient and quickprop training algorithms using the MLP model. Results showed that the networks structures of 11-5-1 (conjugate gradient and quickprop) and 11-3-1 (Levenberg-Marquardt) were the best for estimating areal rainfall using the MLP model. The networks structures of 1-5-11 (conjugate gradient and quickprop) and 1-3-11 (Levenberg-Marquardt), which are the inverse networks for estimating areal rainfall using the best MLP model, were identified for spatial disaggregation of areal rainfall using the MLP model. The KSOFM model was compared with the MLP model for spatial disaggregation of areal rainfall. The MLP and KSOFM models could disaggregate areal rainfall into individual point rainfall with spatial concepts.

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“…The input layer is composed of n input nodes, each connected to all nodes of the Kohonen layer [50][51][52][53][54]. The Kohonen layer consists of [n1-by-n1] matrices.…”

Section: Kohonen Self-organizing Feature Map (Ksofm) Modelmentioning

confidence: 99%

Spatial Disaggregation of Areal Rainfall Using Two Different Artificial Neural Networks Models

Kim

Singh

2015

Water

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“…Their results indicated the SOLO can provide features that facilitate insight into the underlying processes as well as satisfying results. More relevant works can be found in the literature (e.g., [38][39][40][41][42]). …”

Section: Introductionmentioning

confidence: 99%

“…Due to the insufficient data of peak streamflow in size, NN-based models are usually unable to yield satisfactory solutions of extreme values in the streamflow [43]. To overcome this problem, studies that are attempted to improve the quality and the quantity of training data of NN-based models are available in the literature (e.g., [28,40,[44][45][46]). Hence, in a similar manner, an enforced learning strategy is proposed in this paper.…”

Section: Introductionmentioning

confidence: 99%

An Hourly Streamflow Forecasting Model Coupled with an Enforced Learning Strategy

Lin

2015

Water

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Floods, one of the most significant natural hazards, often result in loss of life and property. Accurate hourly streamflow forecasting is always a key issue in hydrology for flood hazard mitigation. To improve the performance of hourly streamflow forecasting, a methodology concerning the development of neural network (NN) based models with an enforced learning strategy is proposed in this paper. Firstly, four different NNs, namely back propagation network (BPN), radial basis function network (RBFN), self-organizing map (SOM), and support vector machine (SVM), are used to construct streamflow forecasting models. Through the cross-validation test, NN-based models with superior performance in streamflow forecasting are detected. Then, an enforced learning strategy is developed to further improve the performance of the superior NN-based models, i.e., SOM and SVM in this study. Finally, the proposed flow forecasting model is obtained. Actual applications are conducted to demonstrate the potential of the proposed model. Moreover, comparison between the NN-based models with and without the enforced learning strategy is performed to evaluate the effect of the enforced learning strategy on model performance. The results indicate that the NN-based models with the enforced learning strategy indeed improve the accuracy of hourly streamflow forecasting. Hence, the presented methodology is expected to be helpful for developing improved NN-based streamflow forecasting models. OPEN ACCESSWater 2015, 7 5877

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“…SOM has attracted increasing interest for water resources application, such as the classification of satellite imagery data and rainfall estimation (Murao et al, 1993), rainfallrounoff modelling (Hsu et al, 2002), typhoon-rainfall forecasting (Lin and Wu, 2009), river flood forecasting (Chang et al, 2007), water resource problems (Kalteh et al, 2008), and model evaluation (Herbst and Casper, 2008;Herbst et al, 2009). The advantages of SOM compared with the other clustering methods have been extensively discussed in the literature (Chen et al, 1995;Mangiameli et al, 1996;Lin and Chen, 2006).…”

Section: Introductionmentioning

confidence: 99%

“…In recent years more hybrid models have been proposed, which combine a clustering technique with local forecasting models that are more accurate since these models are more specialised and have successfully solved many predictions problems, such as a combination of SOM with ANN (Pal et al, 2003;Lin and Wu, 2009;Wang and Yan, 2004), SOM with SVM (Cao, 2003;Fan and Chen, 2006;Fan et al, 2007;Huang and Tsai, 2009), SOM with Radial Basis Function (Lin and Chen, 2005), ANN with K-means (Corzo and Solomantine, 2007) and other models (Chang and Liao, 2006;Chang et al, 2007Chang et al, , 2008. Although the idea of these hybrid models is interesting and promising, it still need to be tested using a river flow time series.…”

Section: Introductionmentioning

confidence: 99%

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

Ismail

Shabri

Samsudin³

2012

Hydrol. Earth Syst. Sci.

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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.

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Enforced self‐organizing map neural networks for river flood forecasting

Cited by 52 publications

References 27 publications

Spatial Disaggregation of Areal Rainfall Using Two Different Artificial Neural Networks Models

Spatial Disaggregation of Areal Rainfall Using Two Different Artificial Neural Networks Models

An Hourly Streamflow Forecasting Model Coupled with an Enforced Learning Strategy

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

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