Monthly streamflow prediction using a hybrid stochastic-deterministic approach for parsimonious non-linear time series modeling

Wang, Zhen; Attar, Nasrin Fathollahzadeh; Khalili, Keivan; Behmanesh, Javad; Band, Shahab S.; Mosavi, Amir; Chau, Kwok-wing

doi:10.1080/19942060.2020.1830858

Cited by 16 publications

(9 citation statements)

References 49 publications

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“…Time series observations can often exhibit correlations with different degrees of non-linearity (Figure 3). This type of behavior is present in almost all real-world time series data, such as in stream-flow forecasting (Wang et al 2020), and in financial markets forecasting (Bukhari et al 2020).…”

Section: Stochastic Behaviormentioning

confidence: 94%

What is the best RNN-cell structure for forecasting each time series behavior?

Khaldi¹,

Afia²,

Chiheb³

et al. 2022

Preprint

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It is unquestionable that time series forecasting is of paramount importance in many fields. The most used machine learning models to address time series forecasting tasks are Recurrent Neural Networks (RNNs).Typically, those models are built using one of the three most popular cells, ELMAN, Long-Short Term Memory (LSTM), or Gated Recurrent Unit (GRU) cells, each cell has a different structure and implies a different computational cost. However, it is not clear why and when to use each RNN-cell structure.Actually, there is no comprehensive characterization of all the possible time series behaviors and no guidance on what RNN cell structure is the most suitable for each behavior. The objective of this study is two-fold: it presents a comprehensive taxonomy of all-time series behaviors (deterministic, random-walk, nonlinear, long-memory, and chaotic), and provides insights into the best RNN cell structure for each time series behavior. We conducted two experiments: (1) The first experiment evaluates and analyzes the role of each component in the LSTM-Vanilla cell by creating 11 variants based on one alteration in its basic architecture (removing, adding, or substituting one cell component). ( 2) The second experiment evaluates and analyzes the performance of 20 possible RNN-cell structures. Our results showed that the MGU-SLIM3 cell is the most recommended for deterministic and nonlinear behaviors, the MGU-SLIM2 cell is the most suitable for random-walk behavior, FB1 cell is advocated for long-memory behavior, and LSTM-SLIM1 for chaotic behavior.

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Section: Stochastic Behaviormentioning

confidence: 94%

What is the best RNN-cell structure for forecasting each time series behavior?

Khaldi¹,

Afia²,

Chiheb³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…To prevent the models from overfitting and underfitting, the objective function for improved GWO-GRU was to minimize the mean value of calibration loss and test loss when the difference of two losses was found within 0.005. In addition, the sequential IGWO-GRU model for five types of output (6,12,24,36, and 72 streamflow values for output, respectively) forecasting with four types of inputs (Ma, Mb, Mc, and Md) was determined by ACF and PACF [38], as shown in Table 2. The main procedure of PACF and ACF are (1) Make PACF and ACF plot with 95% confidence bands.…”

Section: Model Developmentmentioning

confidence: 99%

“…To date, scholars have developed a number of forecasting models using data-driven approaches, such as ANN (artificial neural network) and GEP (gene expression programming), to emulate hydrological behavior because it requires the least information [5,6]. ANN is a widely used model with good performance in comparison to traditional regression models [7].…”

Section: Introductionmentioning

confidence: 99%

Streamflow Forecasting via Two Types of Predictive Structure-Based Gated Recurrent Unit Models

Zhao

Wei

et al. 2021

Water

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Data-intelligent methods designed for forecasting the streamflow of the Fenhe River are crucial for enhancing water resource management. Herein, the gated recurrent unit (GRU) is coupled with the optimization algorithm improved grey wolf optimizer (IGWO) to design a hybrid model (IGWO-GRU) to carry out streamflow forecasting. Two types of predictive structure-based models (sequential IGWO-GRU and monthly IGWO-GRU) are compared with other models, such as the single least-squares support vector machine (LSSVM) and single extreme learning machine (ELM) models. These models incorporate the historical streamflow series as inputs of the model to forecast the future streamflow with data from January 1956 to December 2016 at the Shangjingyou station and from January 1958 to December 2016 at the Fenhe reservoir station. The IGWO-GRU model exhibited a strong ability for mapping in streamflow series when the parameters were carefully tuned. The monthly predictive structure can effectively extract the instinctive hydrological information that is more easily learned by the predictive model than the traditional sequential predictive structure. The monthly IGWO-GRU model was found to be a better forecasting tool, with an average qualification rate of 91.66% in two stations. It also showed good performance in absolute error and peak flow forecasting.

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“…The research showed that the hybrid SETAR-GEP model performed more optimally than the hybrid ARCH-GEP models for the prediction of the monthly streamflow of four rivers. Also, findings proposed several datapreprocessing tasks such as variational mode decomposition (VMD), complete ensemble empirical mode decomposition (CEEMD), and improved CEEMD as data decomposition methods to improve streamflow prediction [27]. Another study revealed that internal pressure is a critical factor for forecasting the discharge coefficient of inflatable dams [28].…”

Section: Introductionmentioning

confidence: 99%

A Case Study of Tidal Analysis Using Theory-Based Artificial Intelligence Techniques for Disaster Management in Taehwa River, South Korea

Kareem

Seong

Kim

et al. 2022

Water

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Monitoring tidal dynamics is imperative to disaster management because it requires a high level of precision to avert possible dangers. Good knowledge of the physical drivers of tides is vital to achieving such a precision. The Taehwa River in Ulsan City, Korea experiences tidal currents in the estuary that drains into the East Sea. The contribution of wind to tide prediction is evaluated by comparing tidal predictions using harmonic analysis and three deep learning models. Harmonic analysis is conducted on hourly water level data from 2010–2021 using the commercial pytides toolbox to generate constituents and predict tidal elevations. Three deep learning models of long short-term memory (LSTM), gated recurrent unit (GRU), and bi-directional lstm (BiLSTM) are fitted to the water level and wind speed to evaluate wind and no-wind scenarios. Results show that Taehwa tides are categorized as semidiurnal tides based on a computed form ratio of 0.2714 in a 24-h tidal cycle. The highest tidal range of 0.60 m is recorded on full moon spring tide indicating the significant lunar pull. Wind effect improved tidal prediction NSE of optimal LSTM model from 0.67 to 0.90. Knowledge of contributing effect of wind will inform flood protection measures to enhance disaster preparedness.

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Monthly streamflow prediction using a hybrid stochastic-deterministic approach for parsimonious non-linear time series modeling

Abstract: wing Chau (2020) Monthly streamflow prediction using a hybrid stochastic-deterministic approach for parsimonious non-linear time series modeling, Engineering

Cited by 16 publications

References 49 publications

What is the best RNN-cell structure for forecasting each time series behavior?

What is the best RNN-cell structure for forecasting each time series behavior?

Streamflow Forecasting via Two Types of Predictive Structure-Based Gated Recurrent Unit Models

A Case Study of Tidal Analysis Using Theory-Based Artificial Intelligence Techniques for Disaster Management in Taehwa River, South Korea

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