Simulation and forecasting of streamflows using machine learning models coupled with base flow separation

Tongal, Hakan; Booij, Martijn J.

doi:10.1016/j.jhydrol.2018.07.004

Cited by 217 publications

(75 citation statements)

References 146 publications

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“…In scheme 5, (1) , and are lower in the dry period and higher in the rainfall period II than those in scheme 1. The results indicate that the performance of the model run is better in the dry period and the rainfall period II because the runoff is usually overestimated in the dry period (Pool et al, 2017;Wang et al, 2017a;Tongal and Booij, 2018;Xiong et al, 2018) and underestimated in the wettest period (Guo et al, 2018;Höge et al, 2018;Pande and Moayeri, 2018;Wang et al, 2018). It is observed that the state variable and the flux have larger effects on simulating runoff than the quick flow ( and ) mode in the rainfall period II.…”

Section: State Variables and Fluxesmentioning

confidence: 94%

Dynamics of hydrological model parameters: mechanisms, problems, and solution

Lan¹,

Lin²,

Xu³

et al. 2019

Preprint

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Abstract. It has been demonstrated that the application of time-varying hydrological model parameters based on dynamic catchment behaviour significantly improves the accuracy and robustness of conventional models. However, the fundamental problems for calibrating dynamic parameters still need to be addressed. In this study, five calibration schemes for dynamic parameters in hydrological models were designed to investigate the underlying causes of poor model performance. The five schemes were assessed with respect to the model performance in different flow phases, the transferability of the dynamic parameters to different time periods, the state variables and fluxes time series, and the response of the dynamic parameter set to the dynamic catchment characteristics. Furthermore, the potential reasons for the poor response of the dynamic parameter set to the catchment dynamics were investigated. The results showed that the underlying causes of poor model performance included time-invariant parameters, compensation among parameters, high dimensionality, and abrupt shifts in the parameters. The recommended calibration scheme exhibited good performance and overcame these problems by characterizing the dynamic behaviour of the catchments. The main reason for the poor response of the dynamic parameter set to the catchment dynamics may be the poor convergence performance of the parameters. In addition, the assessment results of the state variables and fluxes, and the convergence performance of the parameters provided robust indications of the dominant response modes of the hydrological models in different sub-periods or catchments with distinguishing catchment characteristics.

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Section: State Variables and Fluxesmentioning

confidence: 94%

Dynamics of hydrological model parameters: mechanisms, problems, and solution

Lan¹,

Lin²,

Xu³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…These tools forecast forthcoming trends using knowledge-driven decisions resulting from enormous input-output data. The literature includes reviews of the latest machine learning models and comparative studies of the models in river and stream flow forecasting [8][9][10][11][12][13]. Among the machine learning methods used for river flow prediction, machine learning models presented higher performance with better accuracy and generalization ability for river flow as well many hydrological applications [14].…”

Section: Introductionmentioning

confidence: 99%

Support Vector Regression Integrated with Fruit Fly Optimization Algorithm for River Flow Forecasting in Lake Urmia Basin

Samadianfard

Jarhan

Salwana

et al. 2019

Water

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Advancement in river flow prediction systems can greatly empower the operational river management to make better decisions, practices, and policies. Machine learning methods recently have shown promising results in building accurate models for river flow prediction. This paper aims to identify models with higher accuracy, robustness, and generalization ability by inspecting the accuracy of a number of machine learning models. The proposed models for river flow include support vector regression (SVR), a hybrid of SVR with a fruit fly optimization algorithm (FOA) (so-called FOASVR), and an M5 model tree (M5). Additionally, the influence of periodicity (π) on the forecasting enactment was examined. To assess the performance of the proposed models, different statistical meters were implemented, including root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R), and Bayesian information criterion (BIC). Results showed that the FOASVR with RMSE (4.36 and 6.33 m 3 /s), MAE (2.40 and 3.71 m 3 /s) and R (0.82 and 0.81) values had the best performance in forecasting river flows at Babarud and Vaniar stations, respectively. Also, regarding BIC parameters, Q t−1 and π were selected as parsimonious inputs for predicting river flow one month ahead. Overall findings indicated that, although both the FOASVR and M5 predicted the river flows in suitable accordance with observed river flows, the performance of the FOASVR was moderately better than the M5 and periodicity noticeably increased the performance of the models; consequently, FOASVR can be suggested as the most accurate method for forecasting river flows.

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“…Data-driven models attempt to find a relationship between input and output parameters without considering the physical process [1]. Artificial Neural Networks (ANNs) are a type of data-driven model with a flexible mathematical structure which includes both linear and nonlinear concepts which operate within a dynamic input-output system [6].In the past several decades, there has been substantial growth in application of ANNs for R-R modelling [7][8][9][10][11][12][13][14][15] where which ANNs have been compared to other methods, including traditional statistical methods, conceptual models and other artificial intelligence models. Result of these studies have shown that ANN is more accurate than conventional and traditional statistical methods.…”

mentioning

confidence: 99%

“…However, the prediction performance of ANNs relies on an appropriate network structure and input data.A review of previous studies shows that, in some cases, rainfall variables are considered as the only input of the ANN models [26][27][28], while in most studies flow (or water level) antecedents were used as inputs in addition to rainfall [29][30][31][32][33][34][35][36][37]. Some studies also used other input parameters, such as temperature, evaporation, evapotranspiration, or combinations of these parameters in addition to rainfall and/or flow inputs [13,14,36,[38][39][40][41]. In some studies, to use physical and geomorphological characteristics of catchments for estimating surface flow, the GANN model (a three layer feed-forward ANN) was applied.…”

mentioning

confidence: 99%

Rainfall-Runoff Modelling Using Hydrological Connectivity Index and Artificial Neural Network Approach

Asadi

Shahedi

Jarihani

et al. 2019

Water

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The input selection process for data-driven rainfall-runoff models is critical because input vectors determine the structure of the model and, hence, can influence model results.Here, hydro-geomorphic and biophysical time series inputs, including Normalized Difference Vegetation Index (NDVI) and Index of Connectivity (IC; a type of hydrological connectivity index), in addition to climatic and hydrologic inputs were assessed. Selected inputs were used to develop Artificial Neural Networks (ANNs) in the Haughton River catchment and the Calliope River catchment, Queensland, Australia. Results show that incorporating IC as a hydro-geomorphic parameter and remote sensing NDVI as a biophysical parameter, together with rainfall and runoff as hydro-climatic parameters, can improve ANN model performance compared to ANN models using only hydro-climatic parameters. Comparisons amongst different input patterns showed that IC inputs can contribute to further improvement in model performance, than NDVI inputs. Overall, ANN model simulations showed that using IC along with hydro-climatic inputs noticeably improved model performance in both catchments, especially in the Calliope catchment. This improvement is indicated by a slight increase (9.77% and 11.25%) in the Nash-Sutcliffe efficiency and noticeable decrease (24.43% and 37.89%) in the root mean squared error of monthly runoff from Haughton River and Calliope River, respectively. Here, we demonstrate the significant effect of hydro-geomorphic and biophysical time series inputs for estimating monthly runoff using ANN data-driven models, which are valuable for water resources planning and management.2 of 20 models in the simulation of R-R non-linear process is more popular than conceptual models [5]. Data-driven models attempt to find a relationship between input and output parameters without considering the physical process [1]. Artificial Neural Networks (ANNs) are a type of data-driven model with a flexible mathematical structure which includes both linear and nonlinear concepts which operate within a dynamic input-output system [6].In the past several decades, there has been substantial growth in application of ANNs for R-R modelling [7][8][9][10][11][12][13][14][15] where which ANNs have been compared to other methods, including traditional statistical methods, conceptual models and other artificial intelligence models. Result of these studies have shown that ANN is more accurate than conventional and traditional statistical methods. For example, the ANN approach predicted more accurate runoff in the Mississippi River basin than the conventional autoregressive moving average with exogenous inputs (ARMAX) approach in [16]. Birikundavyi et al. [17] have demonstrated that streamflow forecasting using ANN models has better performance than conceptual model (PREVIS) and the conventional autoregressive model coupled with a Kalman filter (ARMAX-KF) models. Additionally, comparison of ANNs with other artificial intelligence models (e.g., support vector machine (SVM), genetic pr...

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Simulation and forecasting of streamflows using machine learning models coupled with base flow separation

Cited by 217 publications

References 146 publications

Dynamics of hydrological model parameters: mechanisms, problems, and solution

Dynamics of hydrological model parameters: mechanisms, problems, and solution

Support Vector Regression Integrated with Fruit Fly Optimization Algorithm for River Flow Forecasting in Lake Urmia Basin

Rainfall-Runoff Modelling Using Hydrological Connectivity Index and Artificial Neural Network Approach

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