Simulating and Predicting of Hydrological Time
Series Based on TensorFlow Deep Learning

Qin, Jinbo; Liang, Jing; Chen, Tao; Lei, Xiaohui; Kang, Aiqing

doi:10.15244/pjoes/81557

Cited by 36 publications

(8 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The accurate determination of model parameters is important for hydrological forecasting to make a model behave closer to the real world [66]. Tensorflow based ML models are also used by many researchers [67]. Therefore, ML models were developed with Keras using Google Tensorflow backend.…”

Section: Models Structure Parameters and Hyperparametersmentioning

confidence: 99%

Applicability of a Three-Stage Hybrid Model by Employing a Two-Stage Signal Decomposition Approach and a Deep Learning Methodology for Runoff Forecasting at Swat River Catchment, Pakistan

Sibtain¹,

Li²,

Azam³

et al. 2020

Pol. J. Environ. Stud.

View full text Add to dashboard Cite

The optimal management of hydropower resources is highly dependent on accurate and reliable hydrological runoff forecasting. The development of a suitable runoff-forecasting model is a challenging task due to the complex and nonlinear nature of runoff. To meet the challenge, this study proposed a three-stage novel hybrid model namely IVG (ICEEMDAN-VMD-GRU), by coupling gated recurrent unit (GRU) with a two-stage signal decomposition methodology, combining improved complete ensemble empirical decomposition with additive noise (ICEEMDAN) and variational mode decomposition (VMD), to forecast the monthly runoff of SWAT river, Pakistan. ICEEMDAN decomposed the runoff time series into subcomponents, and VMD performed further decomposition of the high-frequency component obtained by ICEEMDAN decomposition. Afterward, the GRU network was employed to the decomposed subcomponents for forecasting purposes. The performance of the IVG model was compared with other hybrid models including, ICEEMDAN-VMD-SVM (support vector machine), ICEEMDAN-GRU, VMD-GRU, ICEEMDAN-SVM, VMD-SVM; and standalone models including GRU and SVM by utilizing statistical indices. Experimental results proved that the IVG model outperformed other models in terms of accuracy and error reduction, which indicates the feasibility of the IVG model to analyze the nonlinear features of runoff time series and for runoff forecasting with applicability for future planning and management of water resources.

show abstract

Section: Models Structure Parameters and Hyperparametersmentioning

confidence: 99%

Applicability of a Three-Stage Hybrid Model by Employing a Two-Stage Signal Decomposition Approach and a Deep Learning Methodology for Runoff Forecasting at Swat River Catchment, Pakistan

Sibtain¹,

Li²,

Azam³

et al. 2020

Pol. J. Environ. Stud.

View full text Add to dashboard Cite

show abstract

“…In the past decades, multiple flood forecasting models have been proposed. According to the difference in principle, models for flood forecasting can be divided into process-driven models based on physical mechanisms and data-driven models based on machine learning methods (Douglas-Mankin et al 2010;Qin et al 2018;Yuan et al 2018). Commonly used process-driven models, such as the Xinanjiang model, have been regarded as common techniques for flood process simulation and forecasting (Beven et al 1984;Zhao 1992;Wang et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Application of temporal convolutional network for flood forecasting

et al. 2024

Artificial Intelligence in Hydrology

View full text Add to dashboard Cite

Rainfall–runoff modeling is a complex nonlinear time-series problem in the field of hydrology. Various methods, such as physical-driven and data-driven models, have been developed to study the highly random rainfall–runoff process. In the past 2 years, with the advancement of computing hardware resources and algorithms, deep-learning methods, such as temporal convolutional network (TCN), has been shown good prospects in time-series prediction tasks. The aim of this study is to develop a prediction model based on TCN structure to simulate the hourly rainfall–runoff relationship. We use two datasets in the Jingle and Kuye watersheds to test the model under different network structures and compare with the other four models. The results show that the TCN model outperforms the EIESM, artificial neural network, and long short-term memory and improves the flood forecasting accuracy at different foreseeable periods. It is shown that the TCN has a faster convergence rate and is an effective method for hydrological forecasting.

show abstract

“…Also, Damavandi et al,(2019) found that the LSTM model was better than CaMa-Flood model for daily runoff simulation. In addition, the LSTM model shows better simulation accuracy than the Auto-regression model, which has been widely used to predict the hydrological time series (Qin et al, 2019), and it also showed better results than the Soil and Water Assessment Tool (SWAT) model which is widely used in the water resource field (Fan et al, 2020). Moreover, other previous studies showed that the LSTM model provides better performance of runoff simulation compared to the SIMHYD model, GR4J model (Bai et al, 2021), M5 Cubist model (Shortridge et al 2016), Xinanjing model (Yin et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Is the deep-learning technique a completely alternative for the hydrological model?: A case study on Hyeongsan River Basin, Korea

Kwak

Han²,

Kim

2021

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

It is no doubt that the reliable runoff simulation for proper water resources management is essential. In the past, the runoff was generally modeled from hydrologic models that analyze the rainfall-runoff relationship of the basin.However, since techniques have developed rapidly, it has been attempted to apply especially deep-learning technique for hydrological studies as an alternative to the hydrologic model. The objective of the study is to examine whether the deep-learning technique can completely replace the hydrologic model and show how to improve the performance of runoff simulation using deep-learning technique. The runoff in the Hyeongsan River basin, South Korea from 2013 to 2020 were simulated using two models, 1) Long Short-Term Memory model that is a deep learning technique widely used in the hydrological study and 2) TANK model, and then we compared the runoff modeling results from both models. The results suggested that it is hard to completely replace the hydrological model with the deep-learning technique due to its simulating behavior and discussed how to improve the reliability of runoff simulation results. Also, a method to improve the efficiency of runoff simulation through a hybrid model which is a combination of two approaches, deep-learning technique and hydrologic model was presented.

show abstract

Simulating and Predicting of Hydrological Time Series Based on TensorFlow Deep Learning

Cited by 36 publications

References 29 publications

Applicability of a Three-Stage Hybrid Model by Employing a Two-Stage Signal Decomposition Approach and a Deep Learning Methodology for Runoff Forecasting at Swat River Catchment, Pakistan

Applicability of a Three-Stage Hybrid Model by Employing a Two-Stage Signal Decomposition Approach and a Deep Learning Methodology for Runoff Forecasting at Swat River Catchment, Pakistan

Application of temporal convolutional network for flood forecasting

Is the deep-learning technique a completely alternative for the hydrological model?: A case study on Hyeongsan River Basin, Korea

Contact Info

Product

Resources

About