Forecasting streamflow by combination of a genetic input selection algorithm and wavelet transforms using ANFIS models

Dariane, Alireza B.; Azimi, Sh.

doi:10.1080/02626667.2014.988155

Cited by 40 publications

(22 citation statements)

References 45 publications

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“…Therefore, the fuzzy c-means clustering (FCM) algorithm was employed in this investigation. Further information about FCM algorithm may be found in Dariane and Azimi [35].…”

Section: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

Modeling and Predicting Carbon and Water Fluxes Using Data-Driven Techniques in a Forest Ecosystem

Dou

Yang

2017

Forests

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Accurate estimation of carbon and water fluxes of forest ecosystems is of particular importance for addressing the problems originating from global environmental change, and providing helpful information about carbon and water content for analyzing and diagnosing past and future climate change. The main focus of the current work was to investigate the feasibility of four comparatively new methods, including generalized regression neural network, group method of data handling (GMDH), extreme learning machine and adaptive neuro-fuzzy inference system (ANFIS), for elucidating the carbon and water fluxes in a forest ecosystem. A comparison was made between these models and two widely used data-driven models, artificial neural network (ANN) and support vector machine (SVM). All the models were evaluated based on the following statistical indices: coefficient of determination, Nash-Sutcliffe efficiency, root mean square error and mean absolute error. Results indicated that the data-driven models are capable of accounting for most variance in each flux with the limited meteorological variables. The ANN model provided the best estimates for gross primary productivity (GPP) and net ecosystem exchange (NEE), while the ANFIS model achieved the best for ecosystem respiration (R), indicating that no single model was consistently superior to others for the carbon flux prediction. In addition, the GMDH model consistently produced somewhat worse results for all the carbon flux and evapotranspiration (ET) estimations. On the whole, among the carbon and water fluxes, all the models produced similar highly satisfactory accuracy for GPP, R and ET fluxes, and did a reasonable job of reproducing the eddy covariance NEE. Based on these findings, it was concluded that these advanced models are promising alternatives to ANN and SVM for estimating the terrestrial carbon and water fluxes.

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“…Therefore, the fuzzy c-means clustering (FCM) algorithm was employed in this investigation. Further information about FCM algorithm may be found in Dariane and Azimi [35].…”

Section: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

Modeling and Predicting Carbon and Water Fluxes Using Data-Driven Techniques in a Forest Ecosystem

Dou

Yang

2017

Forests

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“…It has been reported that these hybrid MLMs, which consists of time series decomposition and sub-time series modeling, were able to achieve better performance compared with the single MLMs. Finally, the hybrid MLMs, combined with more than two methods, have been developed for rainfall-runoff and streamflow modeling including DWT, PSO, and SVMs [45]; DWT, GA, and adaptive neuro-fuzzy inference system (ANFIS) [46]; EEMD, PSO, and SVMs [47]; EEMD, SOM, and linear genetic programming [48]; wavelet transform, singular spectrum, chaotic approach, and SVR [49]; and Hermite-projection pursuit regression, social spider optimization, and least square algorithm [50].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

2018

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Accurate modeling for nonlinear and nonstationary rainfall-runoff processes is essential for performing hydrologic practices effectively. This paper proposes two hybrid machine learning models (MLMs) coupled with variational mode decomposition (VMD) to enhance the accuracy for daily rainfall-runoff modeling. These hybrid MLMs consist of VMD-based extreme learning machine (VMD-ELM) and VMD-based least squares support vector regression (VMD-LSSVR). The VMD is employed to decompose original input and target time series into sub-time series called intrinsic mode functions (IMFs). The ELM and LSSVR models are selected for developing daily rainfall-runoff models utilizing the IMFs as inputs. The performances of VMD-ELM and VMD-LSSVR models are evaluated utilizing efficiency and effectiveness indices. Their performances are also compared with those of VMD-based artificial neural network (VMD-ANN), discrete wavelet transform (DWT)-based MLMs (DWT-ELM, DWT-LSSVR, and DWT-ANN) and single MLMs (ELM, LSSVR, and ANN). As a result, the VMD-based MLMs provide better accuracy compared with the single MLMs and yield slightly better performance than the DWT-based MLMs. Among all models, the VMD-ELM and VMD-LSSVR models achieve the best performance in daily rainfall-runoff modeling with respect to efficiency and effectiveness. Therefore, the VMD-ELM and VMD-LSSVR models can be an alternative tool for reliable and accurate daily rainfall-runoff modeling.

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“…Thus physical models are not feasible for wide employment (Aqil, Kita, Yano, & Nishiyama, 2007). In recent years, machine learning methods have been applied to forecasting long-term streamflow, including artificial neural networks (ANNs; Cheng, Feng, Niu, & Liao, 2015;Esmaeelzadeh, Adib, & Alahdin, 2015;Yu, Qin, Larsen, & Chua, 2014), support vector machines (Zhu, Zhou, Ye, & Meng, 2016), fuzzy algorithms (Dariane & Azimi, 2016;Shi, Hu, Yu, & Hu, 2016), and gray system theory (Ma, Li, Zhang, & Fan, 2013). They perform well for stationary time series, whereas hydrological time series are usually nonstationary processes.…”

Section: Introductionmentioning

confidence: 99%

Long‐term streamflow forecasting using artificial neural network based on preprocessing technique

Wang²,

Qiu

2019

Journal of Forecasting

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Artificial neural network (ANN) combined with signal decomposing methods is effective for long‐term streamflow time series forecasting. ANN is a kind of machine learning method utilized widely for streamflow time series, and which performs well in forecasting nonstationary time series without the need of physical analysis for complex and dynamic hydrological processes. Most studies take multiple factors determining the streamflow as inputs such as rainfall. In this study, a long‐term streamflow forecasting model depending only on the historical streamflow data is proposed. Various preprocessing techniques, including empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD) and discrete wavelet transform (DWT), are first used to decompose the streamflow time series into simple components with different timescale characteristics, and the relation between these components and the original streamflow at the next time step is analyzed by ANN. Hybrid models EMD‐ANN, EEMD‐ANN and DWT‐ANN are developed in this study for long‐term daily streamflow forecasting, and performance measures root mean square error (RMSE), mean absolute percentage error (MAPE) and Nash–Sutcliffe efficiency (NSE) indicate that the proposed EEMD‐ANN method performs better than EMD‐ANN and DWT‐ANN models, especially in high flow forecasting.

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Forecasting streamflow by combination of a genetic input selection algorithm and wavelet transforms using ANFIS models

Cited by 40 publications

References 45 publications

Modeling and Predicting Carbon and Water Fluxes Using Data-Driven Techniques in a Forest Ecosystem

Modeling and Predicting Carbon and Water Fluxes Using Data-Driven Techniques in a Forest Ecosystem

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

Long‐term streamflow forecasting using artificial neural network based on preprocessing technique

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