Daily Mean Streamflow Prediction in Perennial and Non-Perennial Rivers Using Four Data Driven Techniques

Abdollahi, Sajjad; Raeisi, Jalil; Khalilianpour, Mohammadreza; Ahmadi, Farshad; Kisi, Özgür

doi:10.1007/s11269-017-1782-7

Cited by 38 publications

(13 citation statements)

References 79 publications

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“…Some studies underlined the importance of meteorological features in drying variability. Abdollahi et al () have shown the importance to combine precipitation and flow patterns for predicting daily mean streamflow of an IRES. De Girolamo, Barca, Pappagallo, and Lo Porto () identified that errors in meteorological inputs are responsible of the limited performance of the model in predicting stream flow and hydrological indicators in an IRES.…”

Section: Discussionmentioning

confidence: 99%

“…To quantify the relevance of the different covariates, the connection weight approach (Olden & Jackson, ; Olden, Joy, & Death, ) is employed,

W_{V} = \sum_{normalh = 1}^{nhu} A_{normalV, normalh} B_{h},

where W V (−) is the relevance of covariate V, A V,h (−) are the ANN coefficients connecting hidden unit h to covariate V, B h (−) are the ANN coefficients connecting hidden unit h to the output, and nhu is the number of hidden units. ANNs have been widely used as black box tools for modelling the rainfall‐runoff transform (see (ASCE, , ) for a review and (Abdollahi, Raeisi, Khalilianpour, Ahmadi, & Kisi, ) for a recent application to an intermittent river).…”

Section: Statistical Framework For Modelling Daily Drying Dynamicsmentioning

confidence: 99%

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A classification approach to reconstruct local daily drying dynamics at headwater streams

Beaufort

Carreau

Sauquet

2019

Hydrological Processes

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Headwater streams (HSs) are generally naturally prone to flow intermittence. These intermittent rivers and ephemeral streams have recently seen a marked increase in interest, especially to assess the impact of drying on aquatic ecosystems. The two objectives of this work are (a) to identify the main drivers of flow intermittence dynamics in HS and (b) to reconstruct local daily drying dynamics. Discrete flow states—“flowing” versus “drying”—are modelled as functions of covariates that include information on climate, hydrology, groundwater, and basin descriptors. Three classifiers to estimate flow states using covariates are tested on four contrasted regions in France: (a) a linear classifier with regularization (LASSO for least absolute shrinkage and selection operator) and two non‐linear non‐parametric classifiers, (b) a one‐hidden‐layer feedforward artificial neural network (ANN) classifier, and (c) a random forest (RF) classifier. The three classifiers are compared with a benchmark classifier (BC) that simply estimates dominant flow state for each month based on observations (without using covariates). The performance assessment over the period 2012–2016 carried out by cross‐validation shows that the three classifiers for flow state based on covariates outperformed the BC. This demonstrates the predictive power of the covariates. ANN is the classifier that globally achieves the best performance to predict the daily drying dynamics whereas both RF and LASSO tend to underestimate the proportion of drying states. The covariates are ranked in terms of relevance for each classifier. The monthly proportion of drying states provided by the discrete observation network has a major importance for the three classifiers ANN, LASSO, and RF. This may reflect the proclivity of a site to flow intermittence. ANN gives higher importance to climatic and hydrological covariates and its non‐linearity allows a greater degree of freedom.

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Section: Discussionmentioning

confidence: 99%

“…To quantify the relevance of the different covariates, the connection weight approach (Olden & Jackson, ; Olden, Joy, & Death, ) is employed,

W_{V} = \sum_{normalh = 1}^{nhu} A_{normalV, normalh} B_{h},

Section: Statistical Framework For Modelling Daily Drying Dynamicsmentioning

confidence: 99%

A classification approach to reconstruct local daily drying dynamics at headwater streams

Beaufort

Carreau

Sauquet

2019

Hydrological Processes

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“…Sabzi et al conducted monthly streamflow modeling utilizing ANFIS, the standalone models of ANN and autoregressive integrated moving average (ARIMA), and an integrated ANN-ARIMA model by using snow telemetry data in Elephant Butte reservoir at Mexico city (Zamani Sabzi et al, 2017). Hydrological parameters, in most cases, have nonlinear behavior between each other, so this is one of the advantages of DDMs to model them in both linear and nonlinear conditions (Abdollahi et al, 2017;Liang et al, 2018;Shiri et al, 2012). GEP model has received much attention in the last few years as a great DDM (Kisi et al, 2014), and it can find full empirical deterministic equations.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Attar

Khalili

et al. 2020

Engineering Applications of Computational Fluid Mechanics

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“…The results indicated that hybrid models outperform the standalone models, particularly in term of high flows. Another attempt was conducted by Abdollahi et al [6] to predict daily mean streamflow of perennial and non-perennial rivers in semi-arid region of Zagros Mountains in Iran using standalone and hybrid models. The applicability of proposed models was evaluated applying several performance metrics.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Decomposition-Based Artificial Intelligence Approach for Daily Streamflow Prediction

Rezaie-Balf

Nowbandegani

Phillips

et al. 2019

Water

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Accurate prediction of daily streamflow plays an essential role in various applications of water resources engineering, such as flood mitigation and urban and agricultural planning. This study investigated a hybrid ensemble decomposition technique based on ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) with gene expression programming (GEP) and random forest regression (RFR) algorithms for daily streamflow simulation across three mountainous stations, Siira, Bilghan, and Gachsar, in Karaj, Iran. To determine the appropriate corresponding input variables with optimal lag time the partial auto-correlation function (PACF) and auto-correlation function (ACF) were used for streamflow prediction purpose. Calibration and validation datasets were separately decomposed by EEMD that eventually improved standalone predictive models. Further, the component of highest pass (IMF1) was decomposed by the VMD approach to breakdown the distinctive characteristic of the variables. Results suggested that the EEMD-VMD algorithm significantly enhanced model calibration. Moreover, the EEMD-VMD-RFR algorithm as a hybrid ensemble model outperformed better than other techniques (EEMD-VMD-GEP, RFR and GEP) for daily streamflow prediction of the selected gauging stations. Overall, the proposed methodology indicated the superiority of hybrid ensemble models compare to standalone in predicting streamflow time series particularly in case of high fluctuations and different patterns in datasets.

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Daily Mean Streamflow Prediction in Perennial and Non-Perennial Rivers Using Four Data Driven Techniques

Cited by 38 publications

References 79 publications

A classification approach to reconstruct local daily drying dynamics at headwater streams

A classification approach to reconstruct local daily drying dynamics at headwater streams

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

An Ensemble Decomposition-Based Artificial Intelligence Approach for Daily Streamflow Prediction

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