Using backpropagation neural networks for flood forecasting in PhraNakhon Si Ayutthaya, Thailand

Soomlek, Chitsutha; Kaewchainam, Nattawadee; Simano, Thawat; So-In, Chakchai

doi:10.1109/icsec.2015.7401424

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…Eventually, its predictability is 91.1% was achieved and its average error squares are 0.0306 (Bui et al, 2016). Soomlek et al, 2015, and his team studied the use of the backpropagation neural network to predict floods in Thailand. They used a neural network algorithm on 278 samples from 2008 to 2010.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of flood using optimized neural network with Gray wolf algorithm (Maroon River case study)

Doumari

2021

Present Environment and Sustainable Development

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Floods, as one of the most frequent natural hazards, cause irreparable damage to infrastructure and agriculture, and housing every year. In order to avoid financial and human losses, the importance of flood forecasting seems inevitable. Considering that floods are caused by many natural and anthropogenic factors and also have limitations such as lack of complete information. In this study, artificial neural networks have been used as an efficient method for flood prediction. The neural network inputs include the Dubai River and the Eshel River, this data was collected over 8 Years from the Maroon River. The network used is a multilayer perceptron, also the neural network weights are optimized by the Gray wolf algorithm and the results are compared with other common methods. Analysis of the output results shows that the neural network with the Gray Wolf algorithm has better results than neural network and Genetic algorithms and the error of this method is 0.53%, which indicates high accuracy and precision for flood prediction compared to other evolutionary algorithms. This method is used to obtain the best amount of data for testing and training. As the results, the best rate is 80% for training and 20% for testing. Obtained results show the neural network error squares with 80% of the training data and 20% of the test data.

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Section: Introductionmentioning

confidence: 99%

“…They evaluated average squares error, correlation coefficient, and productivity. The predictability in their method is 90% and its average error squared is 0.1139 (Soomlek et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Prediction of flood using optimized neural network with Gray wolf algorithm (Maroon River case study)

Doumari

2021

Present Environment and Sustainable Development

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“…A BPNN is commonly implemented in different types of research related to ANNs. Several studies on the BPNN were conducted in the field of hydrology and one of them was a case study on the lower Chao Phraya River Basin [17]. This study showed that the prediction model composed of the water level, the water flow rate, rainfall, and the height of the basin as the input factor, showed high accuracy in water level prediction.…”

Section: Introductionmentioning

confidence: 99%

A Promising Wavelet Decomposition –NNARX Model to Predict Flood

Anuar

Rahman

Soh

et al. 2020

Int. j. electr. comput. eng. syst. (Online)

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Flood is a major disaster that happens around the world. It has caused many casualties and massive destruction of property. Estimating the chance of a flood occurring depends on several factors, such as rainfall, the structure and the flow rate of the river. This research used the neural network autoregressive exogenous input (NNARX) model to predict floods. One of the research challenges was to develop accurate models and improve the forecasting model. This research aimed to improve the performance of the neural network model for flood prediction. A new technique was proposed for modelling nonlinear data of flood forecasting using the wavelet decomposition-NNARX approach. This paper discusses the process of identifying the parameters involved to make a forecast as the rainfall value requires the flow rate of the river and its water level. The original data were processed by wavelet decomposition and filtered to generate a new set of data for the NNARX prediction model where the process can be compared. This research compared the performance of the wavelet and the non-wavelet NNARX model. Experimental results showed that the proposed approach had better performance testing results in relation to its counterpart in terms of hourly forecast, with the mean square error (MSE) of 2.0491e-4 m2 compared to 6.1642e-4 m2, respectively. The proposed approach was also studied for long-term forecast up to 5 years, where the obtained MSE was higher, i.e., 0.0016 m2.

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Water level prediction model using back propagation neural network: Case study: The lower of chao phraya basin

Truatmoraka

Waraporn

Suphachotiwatana

2016

2016 4th International Symposium on Computational and Business Intelligence (ISCBI)

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Using backpropagation neural networks for flood forecasting in PhraNakhon Si Ayutthaya, Thailand

Cited by 3 publications

References 8 publications

Prediction of flood using optimized neural network with Gray wolf algorithm (Maroon River case study)

Prediction of flood using optimized neural network with Gray wolf algorithm (Maroon River case study)

A Promising Wavelet Decomposition –NNARX Model to Predict Flood

Water level prediction model using back propagation neural network: Case study: The lower of chao phraya basin

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