Univariate streamflow forecasting using commonly used data-driven models: literature review and case study

Zhang, Zhenghao; Zhang, Qiang; Singh, Vijay P.

doi:10.1080/02626667.2018.1469756

Cited by 84 publications

(36 citation statements)

References 116 publications

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“…Artificial neural network (ANN) is the most common machine learning model that has found application in streamflow simulation over the last two decades [1,37]. It is known for modelling complex input-output relationships inherent in hydrological time series features within a river catchment.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…One of the emerging research areas in hydrology is hydrological simulation [1], through which catchment responses are evaluated in terms of meteorological forcing variables. Hydrological simulation is also crucial for water resource planning and management, such as flood prevention, water supply distribution, hydraulic structure design, and reservoir operation [2,3].…”

Section: Introductionmentioning

confidence: 99%

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Multivariate Streamflow Simulation Using Hybrid Deep Learning Models

Wegayehu

Muluneh

2021

Computational Intelligence and Neuroscience

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Reliable and accurate streamflow simulation has a vital role in water resource development, mainly in agriculture, environment, domestic water supply, hydropower generation, flood control, and early warning systems. In this context, these days, deep learning algorithms have got enormous attention due to their high-performance simulation capacity. In this study, we compared multilayer perceptron (MLP), long short-term memory (LSTM), and gated recurrent unit (GRU) with the proposed new hybrid models, including CNN-LSTM and CNN-GRU. Hence, we can simulate one-step daily streamflow in different agroclimatic conditions, rolling time windows, and a range of variable input combinations. The analysis used daily multivariate and multisite time series data collected from Awash River Basin (Borkena watershed: Ethiopia) and Tiber River Basin (Upper Tiber River Basin: Italy) stations. The datasets were subjected to rigorous quality control processes. Consequently, it rolled to a different time lag to remove noise in the time series and further split into training and testing datasets using a ratio of 80 : 20, respectively. Finally, the results showed that integrating the GRU layer with the convolutional layer and using monthly rolled average daily input time series could substantially improve the simulation of streamflow time series.

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multivariate Streamflow Simulation Using Hybrid Deep Learning Models

Wegayehu

Muluneh

2021

Computational Intelligence and Neuroscience

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“…The data-driven stream-flow prediction model captures linear and nonlinear relationships between stream-flow, rainfall, climate indices, and related inputs [13], [24]. Conventional black box time series models such as least squares (LS), autoregressive (AR), autoregressive moving average (ARMA), multiple linear regression (MLR), and stepwise cluster analysis (SCA) have been applied to hydrological forecasting [25]. However, these models cannot handle nonlinear hydrological processes.…”

Section: B Data Driven Model Based On Ai Algorithmmentioning

confidence: 99%

Flood Prediction Using Rainfall-Flow Pattern in Data-Sparse Watersheds

et al. 2020

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Real-time flood forecasting of small-and medium-sized rivers in areas with scarce hydrological data is an urgent problem that needs to be solved. Traditional hydrological model parameters cannot be fully trained owing to a lack of data; thus, results obtained by such models are not satisfactory. We need a new way to solve the forecasting problem for small-and medium-sized rivers. We found that the time series of some feature variables have evident change trajectories in spatial dimension, and the change of some feature variables in the spatial dimension has a decisive influence on flooding processes, such as the spatial distribution of rainfall. To reflect the change of feature variables in spatial dimension with to solve the problem of the lack of hydrological data, we constructed a rainfall-flow pattern composed of a spatial-temporal dynamic time warping algorithm and multi-feature algorithm to measure the similarity of hydrological time series. In the experimental watersheds, we used rainfall-flow patterns to forecast the shortterm flood streamflow, and satisfactory results were obtained. This suggests that the algorithm is suitable for hydrological studies and improves the accuracy of real-time flood forecasting for longer forecast periods. INDEX TERMS Spatiotemporal sequence data, rainfall-flow pattern matching, similarity measurement algorithm, multi-feature algorithm, ST-DTW algorithm.

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“…Streamflow forecasting is important due to its engineeringoriented implementation in flood and water resources management. The large variety of relevant applications includes flood and drought prediction, irrigation and reservoir operation applications (see, for example, Zhang et al, 2018). Therefore, improved hydrological forecasts in various time scales can benefit the society.…”

Section: Introductionmentioning

confidence: 99%

“…Data-driven, including machine learning, models are commonly used for streamflow (or river discharge and reservoir inflow) forecasting. The latter can be performed by exclusively using observed stream-flow data, as in Papacharalampous et al (2017aPapacharalampous et al ( , 2018a and Zhang et al (2018), or by also using information obtained from predictor variables (e.g. precipitation variables).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of random forests and Prophet for daily streamflow forecasting

Papacharalampous

Tyralis

2018

Adv. Geosci.

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Abstract. We assess the performance of random forests and Prophet in forecasting daily streamflow up to seven days ahead in a river in the US. Both the assessed forecasting methods use past streamflow observations, while random forests additionally use past precipitation information. For benchmarking purposes we also implement a naïve method based on the previous streamflow observation, as well as a multiple linear regression model utilizing the same information as random forests. Our aim is to illustrate important points about the forecasting methods when implemented for the examined problem. Therefore, the assessment is made in detail at a sufficient number of starting points and for several forecast horizons. The results suggest that random forests perform better in general terms, while Prophet outperforms the naïve method for forecast horizons longer than three days. Finally, random forests forecast the abrupt streamflow fluctuations more satisfactorily than the three other methods.

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Univariate streamflow forecasting using commonly used data-driven models: literature review and case study

Cited by 84 publications

References 116 publications

Multivariate Streamflow Simulation Using Hybrid Deep Learning Models

Multivariate Streamflow Simulation Using Hybrid Deep Learning Models

Flood Prediction Using Rainfall-Flow Pattern in Data-Sparse Watersheds

Evaluation of random forests and Prophet for daily streamflow forecasting

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