An adaptive middle and long-term runoff forecast model using EEMD-ANN hybrid approach

Tan, Qiaofeng; Lei, Xiaohui; Wang, Xu; Wang, Hao; Wen, Xin; Ji, Yi; Kang, Ai-Qin

doi:10.1016/j.jhydrol.2018.01.015

Cited by 190 publications

(60 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Araghinejad [219] Reference [220] contributed to improving decomposition ensemble prediction models by developing an EEMD-ANN model for monthly prediction. The performance comparison with SVM, ANFIS, and ANNs showed a significant improvement in accuracy.…”

Section: Long-term Flood Prediction Using Hybrid ML Methodsmentioning

confidence: 99%

Flood Prediction Using Machine Learning Models: Literature Review

Mosavi

Öztürk

Chau

2018

Water

976

469

View full text Add to dashboard Cite

Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models contributed to risk reduction, policy suggestion, minimization of the loss of human life, and reduction 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 contributed highly in the advancement of prediction systems providing better performance and cost-effective solutions. Due to the vast benefits and potential of ML, its popularity dramatically increased among hydrologists.Researchers through introducing novel ML methods and hybridizing of the existing ones aim at discovering more accurate and efficient prediction models. The main contribution of this paper is to demonstrate the state of the art of ML models in flood prediction and to give insight into the most suitable models. In this paper, the literature where ML models were benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed are particularly investigated to provide an extensive overview on the various ML algorithms used in the field. The performance comparison of ML models presents an in-depth understanding of the different techniques within the framework of a comprehensive evaluation and discussion. As a result, this 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 as the most effective strategies for the improvement of ML methods. This survey can be used as a guideline for hydrologists as well as climate scientists in choosing the proper ML method according to the prediction task. Keywords: flood prediction; flood forecasting; flash-flood model, big flood management; hydrologic model; rainfall-runoff, hybrid & ensemble machine learning; artificial neural networks (ANNs); support vector machines (SVM); natural hazards & disasters; adaptive neuro-fuzzy inference system (ANFIS); decision trees (DT); internet of things (IoT); random forest (RF); survey; classification and regression trees (CART), data science; deep learning; big data; bagging, boosting, artificial intelligence (AI); soft computing; extreme event management; time series prediction; multilayer perceptron (MLP); simulated annealing (SA); multivariate adaptive regression splines (MARS), supervised learning IntroductionAmong the natural disasters, floods are the most destructive, causing massive damage to human life, infrastructure, agriculture, and the socioeconomic system.Governments, therefore, are under pressure to develop reliable and accurate maps of flood risk areas and further plan for sustainable flood risk management focusing on prevention, protection, and preparedness [1]. Flood prediction models are of significant impor...

show abstract

Section: Long-term Flood Prediction Using Hybrid ML Methodsmentioning

confidence: 99%

Flood Prediction Using Machine Learning Models: Literature Review

Mosavi

Öztürk

Chau

2018

Water

976

469

View full text Add to dashboard Cite

show abstract

“…Several studies have demonstrated that a skillful streamflow forecast can enhance the efficiency of water allocation systems to manage the trade-off between hydropower, irrigation, municipal, and environmental services [28][29][30][31]. The potential for employing seasonal forecast in the Yangtze River basin has been investigated in several research studies, mostly through statistical techniques [32][33][34]. However, the potential application of the available seasonal forecasts for reservoir impoundment operation is not well understood in the Yangtze River basin.…”

mentioning

confidence: 99%

Evaluation of GloFAS-Seasonal Forecasts for Cascade Reservoir Impoundment Operation in the Upper Yangtze River

Chen

Guo

Wang

et al. 2019

Water

View full text Add to dashboard Cite

Standard impoundment operation rules (SIOR) are pre-defined guidelines for refilling reservoirs before the end of the wet season. The advancement and availability of the seasonal flow forecasts provide the opportunity for reservoir operators to use flexible and early impoundment operation rules (EIOR). These flexible impoundment rules can significantly improve water conservation, particularly during dry years. In this study, we investigate the potential application of seasonal streamflow forecasts for employing EIOR in the upper Yangtze River basin. We first define thresholds to determine the streamflow condition in September, which is an important period for decision-making in the basin, and then select the most suitable impoundment operation rules accordingly. The thresholds are used in a simulation-optimization model to evaluate different scenarios for EIOR and SIOR by multiple objectives. We measure the skill of the GloFAS-Seasonal forecast, an operational global seasonal river flow forecasting system, to predict streamflow condition according to the selected thresholds. The results show that: (1) the 20th and 30th percentiles of the historical September flow are suitable thresholds for evaluating the possibility of employing EIOR;(2) compared to climatological forecasts, GloFAS-Seasonal forecasts are skillful for predicting the streamflow condition according to the selected 20th and 30th percentile thresholds; and (3) during dry years, EIOR could improve the fullness storage rate by 5.63% and the annual average hydropower generation by 4.02%, without increasing the risk of flooding. GloFAS-Seasonal forecasts and early reservoir impoundment have the potential to enhance hydropower generation and water utilization. events [3,4]. Conventionally, during wet season, reservoir operators release water preferentially for flood control [5,6] while storing water before the end of wet season to meet the demand for hydropower generation, navigation, and water supply. For full replenishment of storage, reservoir operators and academic researchers have highlighted the importance of reservoir impoundment operation and impoundment rules in several studies [7][8][9][10].Reservoir operation rules are often used to provide guidelines for reservoir operators to determine the amount of controlled discharge. Among these rules, the reservoir impoundment rules are designed to refill the reservoir and raise the storage water level. The New York City rule [11] is among the early guidelines for reservoir impoundment and provides the probability of spills rather than the amount of spill to minimize the water shortage [12]. Since the development of the New York City rule, different types of reservoir impoundment operation rules have been developed and employed for various reservoir systems [7,[13][14][15][16][17]. In the Yangtze River, which is one of the largest rivers in the world by discharge volume with huge reservoir storage capacity, the impoundment operation is complex and challenging. Water managers and stakeholders employ prede...

show abstract

“…Other relevant research contributions are those of Zhang et al (2015), Du et al (2017) , Tan et al (2018), Quilty and Adamowski (2018), and Fang et al (2019) who recently pointed out and explicitly criticized the afore-mentioned unpractical (and even incorrect) usage of signal processing techniques. Zhang et al (2015) evaluated and compared the outcomes of hindcast and forecast experiments (with and without validation information, respectively) for decomposition models based on WA, EMD, SSA, ARMA and ANN.…”

Section: Introductionmentioning

confidence: 99%

“…Du et al (2017) demonstrated that the usage of SSA and the discrete wavelet transform (DWT) directly on the entire hydrological time series is incorrect. Tan et al (2018) assessed the impractical usage of forecasting scheme based on the EEMD and ANN. Quilty and Adamowski (2018) addressed the incorrect usage of the waveletbased models for hydrological forecasting.…”

Section: Introductionmentioning

confidence: 99%

Two-stage Variational Mode Decomposition and Support Vector Regression for Streamflow Forecasting

Zuo¹,

Luo²,

Wang³

et al. 2019

Preprint

View full text Add to dashboard Cite

Abstract. Streamflow forecasting is a crucial component in the management and control of water resources. Decomposition-based approaches have particularly demonstrated improved streamflow forecasting performance. However, it is not practical to firstly decompose the entire streamflow into several signal components and then divide the data samples of each component into training and validation sets for signal component prediction. This impracticality is due to the fact that some validation information, that is not available in practical streamflow forecasting, is used in that training process. Unfortunately, firstly dividing the entire streamflow into training and validation sets and then decomposing each set separately lead to undesirable boundary effects and complicated forecasting. Moreover, establishing a model for each signal component is quite laborious and summing the component predictions may lead to error accumulation. In addition, summing the decomposition results may sometimes lead to inaccurate reconstruction of the original streamflow. In order to address these shortcomings of decomposition-based models and improve the forecasting performance in basins lacking meteorological observations (e.g., precipitation and temperature), we propose a two-stage decomposition prediction (TSDP) framework, realize this framework using variational mode decomposition (VMD) and support vector machines (SVR), and refer to this realization as VMD-SVR. In the first stage of the TSDP framework, the entire streamflow data was divided into training and validation sets, each of which was then separately decomposed to avoid the influence of validation information on training. In the second stage, a single model for streamflow prediction was established using a set of mixed shuffled samples. This scheme saves the modelling time and reduces the influence of the boundary effects. We demonstrate experimentally the effectiveness, efficiency and reliability of the TSDP framework and its VMD-SVR realization in terms of the boundary effect reduction, decomposition performance, prediction outcomes, time consumption, overfitting, and forecasting capability for long leading times. Specifically, five comparative experiments were conducted based on the ensemble empirical mode decomposition (EEMD), singular spectrum analysis (SSA), discrete wavelet transform (DWT) and SVR. The experimental results on monthly runoff collected from three stations at the Wei River show the superiority of the TSDP framework compared to benchmark models.

show abstract

An adaptive middle and long-term runoff forecast model using EEMD-ANN hybrid approach

Cited by 190 publications

References 43 publications

Flood Prediction Using Machine Learning Models: Literature Review

Flood Prediction Using Machine Learning Models: Literature Review

Evaluation of GloFAS-Seasonal Forecasts for Cascade Reservoir Impoundment Operation in the Upper Yangtze River

Two-stage Variational Mode Decomposition and Support Vector Regression for Streamflow Forecasting

Contact Info

Product

Resources

About