Discharge forecasting using an Online Sequential Extreme Learning Machine (OS-ELM) model: A case study in Neckar River, Germany

Yadav, Basant; Ch, Sudheer; Mathur, Shashi; Adamowski, Jan

doi:10.1016/j.measurement.2016.06.042

Cited by 76 publications

(21 citation statements)

References 71 publications

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“…An improved ELM model is developed in a new version called online sequential extreme learning machine (OS-ELM) for multiple river flow scales prediction in Canada [22]. e same OS-ELM model was developed for flood events forecasting for hourly river flow monitoring [23]. e findings of the improved ELM model demonstrated a noticeable prediction performance.…”

Section: Introductionmentioning

confidence: 99%

Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Faris

Al‐Ansari

2020

Complexity

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e capability of the extreme learning machine (ELM) model in modeling stochastic, nonlinear, and complex hydrological engineering problems has been proven remarkably. e classical ELM training algorithm is based on a nontuned and random procedure that might not be efficient in convergence of excellent performance or possible entrapment in the local minima problem. is current study investigates the integration of a newly explored metaheuristic algorithm (i.e., Salp Swarm Algorithm (SSA)) with the ELM model to forecast monthly river flow. Twenty years of river flow data time series of the Tigris river at the Baghdad station, Iraq, is used as a case study. Different input combinations are applied for constructing the predictive models based on antecedent values. e results are evaluated based on several statistical measures and graphical presentations. e river flow forecast accuracy of SSA-ELM outperformed the classical ELM and other artificial intelligence (AI) models. Over the testing phase, the proposed SSA-ELM model yielded a satisfactory enhancement in the level accuracies (8.4 and 13.1 percentage of augmentation for RMSE and MAE, respectively) against the classical ELM model. In summary, the study ascertains that the SSA-ELM model is a qualified data-intelligent model for monthly river flow prediction at the Tigris river, Iraq.

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Section: Introductionmentioning

confidence: 99%

Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Faris

Al‐Ansari

2020

Complexity

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“…Several kernel functions, such as linear, sigmoid, polynomial, and radial functions, are available for SVR. However, various hydrometeorological studies show a favorable performance with radial basis kernel function [107][108][109]. In addition, the radial basis function (RBF) can effectively handle the nonlinear relation between inputs and output effectively.…”

Section: Selection Of Kernel Function For Svrmentioning

confidence: 99%

A Machine Learning Approach for Improving Near-Real-Time Satellite-Based Rainfall Estimates by Integrating Soil Moisture

et al. 2019

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Near-real-time (NRT) satellite-based rainfall estimates (SREs) are a viable option for flood/drought monitoring. However, SREs have often been associated with complex and nonlinear errors. One way to enhance the quality of SREs is to use soil moisture information. Few studies have indicated that soil moisture information can be used to improve the quality of SREs. Nowadays, satellite-based soil moisture products are becoming available at desired spatial and temporal resolutions on an NRT basis. Hence, this study proposes an integrated approach to improve NRT SRE accuracy by combining it with NRT soil moisture through a nonlinear support vector machine-based regression (SVR) model. To test this novel approach, Ashti catchment, a sub-basin of Godavari river basin, India, is chosen. Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA)-based NRT SRE 3B42RT and Advanced Scatterometer-derived NRT soil moisture are considered in the present study. The performance of the 3B42RT and the corrected product are assessed using different statistical measures such as correlation coefficient (CC), bias, and root mean square error (RMSE), for the monsoon seasons of 2012–2015. A detailed spatial analysis of these measures and their variability across different rainfall intensity classes are also presented. Overall, the results revealed significant improvement in the corrected product compared to 3B42RT (except CC) across the catchment. Particularly, for light and moderate rainfall classes, the corrected product showed the highest improvement (except CC). On the other hand, the corrected product showed limited performance for the heavy rainfall class. These results demonstrate that the proposed approach has potential to enhance the quality of NRT SRE through the use of NRT satellite-based soil moisture estimates.

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“…Compared with other popular online learning algorithms, OSELM can provide better generalization performance at a much faster learning speed. Depending upon these advantages, OSELM has been successfully applied in the field of system modeling and prediction, such as online nonlinear system identification [8,9], ship roll motion prediction [10], consumer sentiments prediction [11], and time series prediction [12][13][14][15]. Despite an excellent online learning algorithm, the OSELM may still suffer from a drawback of instability due to the potential ill-conditioned matrix inversion, and its stability and generalization performance could be greatly influenced once the autocorrelation matrix of the hidden layer output matrix is singular or ill-conditioning.…”

Section: Introductionmentioning

confidence: 99%

Online Sequential Extreme Learning Machine with Generalized Regularization and Adaptive Forgetting Factor for Time-Varying System Prediction

Wei

Tang

et al. 2018

Mathematical Problems in Engineering

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Many real world applications are of time-varying nature and an online learning algorithm is preferred in tracking the realtime changes of the time-varying system. Online sequential extreme learning machine (OSELM) is an excellent online learning algorithm, and some improved OSELM algorithms incorporating forgetting mechanism have been developed to model and predict the time-varying system. But the existing algorithms suffer from a potential risk of instability due to the intrinsic ill-posed problem; besides, the adaptive tracking ability of these algorithms for complex time-varying system is still very weak. In order to overcome the above two problems, this paper proposes a novel OSELM algorithm with generalized regularization and adaptive forgetting factor (AFGR-OSELM). In the AFGR-OSELM, a new generalized regularization approach is employed to replace the traditional exponential forgetting regularization to make the algorithm have a constant regularization effect; consequently the potential illposed problem of the algorithm can be completely avoided and a persistent stability can be guaranteed. Moreover, the AFGR-OSELM adopts an adaptive scheme to adjust the forgetting factor dynamically and automatically in the online learning process so as to better track the dynamic changes of the time-varying system and reduce the adverse effects of the outdated data in time; thus it tends to provide desirable prediction results in time-varying environment. Detailed performance comparisons of AFGR-OSELM with other representative algorithms are carried out using artificial and real world data sets. The experimental results show that the proposed AFGR-OSELM has higher prediction accuracy with better stability than its counterparts for predicting time-varying system.

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Discharge forecasting using an Online Sequential Extreme Learning Machine (OS-ELM) model: A case study in Neckar River, Germany

Cited by 76 publications

References 71 publications

Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

A Machine Learning Approach for Improving Near-Real-Time Satellite-Based Rainfall Estimates by Integrating Soil Moisture

Online Sequential Extreme Learning Machine with Generalized Regularization and Adaptive Forgetting Factor for Time-Varying System Prediction

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