Improving Ann‐Based Short‐Term and Long‐Term Seasonal River Flow Forecasting with Signal Processing Techniques

Badrzadeh, Honey; Sarukkalige, Ranjan; Jayawardena, A. W.

doi:10.1002/rra.2865

Cited by 20 publications

(17 citation statements)

References 55 publications

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“…Secondly, most classical statistical methods cannot forecast the flood peak discharge precisely, which is the most important motivation for streamflow prediction. Last but not least, most research towards river flow prediction is yearly [11], [12], seasonal [13],daily [14], [15] or even hourly [16]. However, the flood season on the Yangtze River usually lasts 2-3 months within a year.…”

Section: Introductionmentioning

confidence: 99%

Streamflow Prediction Using Deep Learning Neural Network: Case Study of Yangtze River

Liu

Jiang

et al. 2020

IEEE Access

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The most important motivation for streamflow forecasts is flood prediction and longtime continuous prediction in hydrological research. As for many traditional statistical models, forecasting flood peak discharge is nearly impossible. They can only get acceptable results in normal year. On the other hand, the numerical methods including physics mechanisms and rainfall-atmospherics could provide a better performance when floods coming, but the minima prediction period of them is about one month ahead, which is too short to be used in hydrological application. In this study, a deep neural network was employed to predict the streamflow of the Hankou Hydrological Station on the Yangtze River. This method combined the Empirical Mode Decomposition (EMD) algorithm and Encoder Decoder Long Short-Term Memory (En-De-LSTM) architecture. Owing to the hydrological series prediction problem usually contains several different frequency components, which will affect the precision of the longtime prediction. The EMD technique could read and decomposes the original data into several different frequency components. It will help the model to make longtime predictions more efficiently. The LSTM based En-De-LSTM neural network could make the forecasting closer to the observed in peak flow value through reading, training, remembering the valuable information and forgetting the useless data. Monthly streamflow data (from January 1952 to December 2008) from Hankou Hydrological Station on the Yangtze River was selected to train the model, and predictions were made in two years with catastrophic flood events and ten years rolling forecast. Furthermore, the Root Mean Square Error (RMSE), Coefficient of Determination (R 2), Willmott's Index of agreement (WI) and the Legates-McCabe's Index (LMI) were used to evaluate the goodness-of-fit and performance of this model. The results showed the reliability of this method in catastrophic flood years and longtime continuous rolling forecasting. INDEX TERMS Yangtze River, hydrological time series forecasting, streamflow prediction, empirical mode decomposition, deep learning.

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Section: Introductionmentioning

confidence: 99%

Streamflow Prediction Using Deep Learning Neural Network: Case Study of Yangtze River

Liu

Jiang

et al. 2020

IEEE Access

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“…In other cases, it has been seen that the performance of models are highly increased through decomposition to produce cleaner inputs. For example, wavelet-neuro-fuzzy models [206] are significantly more accurate and faster than the single ANFIS and ANNs. Nonetheless, with an increases in the lead-time the uncertainty in prediction increases.…”

Section: Comparative Performance Analysis and Discussionmentioning

confidence: 99%

“…Here, it is also worth mentioning the importance of further signal processing techniques e.g. [206] in both long-term and short-term flood.…”

Section: Comparative Performance Analysis and Discussionmentioning

confidence: 99%

Flood Prediction Using Machine Learning, Literature Review

Mosavi¹,

Öztürk²,

Kwok-wing³

2018

Preprint

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Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models has been contributing to risk reduction, policy suggestion, minimizing loss of human life and reducing the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods have highly contributed in the advancement of prediction systems providing better performance and cost effective solutions. Due to the vast benefits and potential of ML, its popularity has dramatically increased among hydrologists. Researchers through introducing the novel ML methods and hybridization of the existing ones have been aiming at discovering more accurate and efficient prediction models. The main contribution is to demonstrate the state of the art of ML models in flood prediction and give an insight over the most suitable models. The literature where ML models are benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed have been particularly investigated to provide an extensive overview on various ML algorithms usage in the field. The performance comparison of ML models presents an in-depth understanding about the different techniques within the framework of a comprehensive evaluation and discussion. As the result, the paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported the most effective strategy in improvement of the ML methods. This survey can be used as a guideline for the hydrologists as well as climate scientists to assist them choosing the proper ML method according to the prediction task conclusions.

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“…Liong et al, 2000;Şen and Altunkaynak, 2006;Firat et al, 2009;Turan and Yurdusev, 2014;Jayawardena et al, 2014). More recently, adaptive neuro-fuzzy system, or ANFIS (Jang, 1993) which has the advantages of both neural networks and fuzzy reasoning techniques has found applications in hydrology including river flow forecasting (Chiang et al, 2004;Vernieuwe, 2005;Chang and Chang, 2006;Aqil et al, 2007;Firat et al, 2009;Keskin et al, 2006;Nayak et al, 2004;Talei et al, 2010;Sanikhani and Kisi, 2012;Badrzadeh et al, 2014).…”

Section: Introductionmentioning

confidence: 97%

Hourly runoff forecasting for flood risk management: Application of various computational intelligence models

Badrzadeh

Sarukkalige

Jayawardena

2015

Journal of Hydrology

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Improving Ann‐Based Short‐Term and Long‐Term Seasonal River Flow Forecasting with Signal Processing Techniques

Cited by 20 publications

References 55 publications

Streamflow Prediction Using Deep Learning Neural Network: Case Study of Yangtze River

Streamflow Prediction Using Deep Learning Neural Network: Case Study of Yangtze River

Flood Prediction Using Machine Learning, Literature Review

Hourly runoff forecasting for flood risk management: Application of various computational intelligence models

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