Support vector regression optimized by meta-heuristic algorithms for daily streamflow prediction

Malik, Anurag; Tikhamarine, Yazid; Souag-Gamane, Doudja; Kişi, Özgür; Pham, Quoc Bao

doi:10.1007/s00477-020-01874-1

Cited by 97 publications

(27 citation statements)

References 106 publications

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“…Accordingly, the original SVM algorithm is extended for nonlinear classification via the use of kernel functions [81]. Kernel functions, which are often nonlinear, map the space of input variables into a much higher-dimensional space, where separable hyperplanes are more likely to exist [82,83]. In addition, soft margin is proposed to handle the case the hyperplane does not exist even in the higher-dimensional space [53].…”

Section: Support Vector Machine (Svm) For Pattern Classificationmentioning

confidence: 99%

Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification

Dinh

Nguyen

Tran

et al. 2021

Mathematical Problems in Engineering

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Soil erosion induced by rainfall is a critical problem in many regions in the world, particularly in tropical areas where the annual rainfall amount often exceeds 2000 mm. Predicting soil erosion is a challenging task, subjecting to variation of soil characteristics, slope, vegetation cover, land management, and weather condition. Conventional models based on the mechanism of soil erosion processes generally provide good results but are time-consuming due to calibration and validation. The goal of this study is to develop a machine learning model based on support vector machine (SVM) for soil erosion prediction. The SVM serves as the main prediction machinery establishing a nonlinear function that maps considered influencing factors to accurate predictions. In addition, in order to improve the accuracy of the model, the history-based adaptive differential evolution with linear population size reduction and population-wide inertia term (L-SHADE-PWI) is employed to find an optimal set of parameters for SVM. Thus, the proposed method, named L-SHADE-PWI-SVM, is an integration of machine learning and metaheuristic optimization. For the purpose of training and testing the method, a dataset consisting of 236 samples of soil erosion in Northwest Vietnam is collected with 10 influencing factors. The training set includes 90% of the original dataset; the rest of the dataset is reserved for assessing the generalization capability of the model. The experimental results indicate that the newly developed L-SHADE-PWI-SVM method is a competitive soil erosion predictor with superior performance statistics. Most importantly, L-SHADE-PWI-SVM can achieve a high classification accuracy rate of 92%, which is much better than that of backpropagation artificial neural network (87%) and radial basis function artificial neural network (78%).

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Section: Support Vector Machine (Svm) For Pattern Classificationmentioning

confidence: 99%

Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification

Dinh

Nguyen

Tran

et al. 2021

Mathematical Problems in Engineering

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“…It enthused their basic concept from the behaviors of bird flocks. The PSO could be used in different fields of optimization, such as multiple-objective optimization, nonlinear and stochastic problems (Malik et al, 2020d;Tikhamarine et al, 2019Tikhamarine et al, , 2020. The working assembly of PSO could be summarized in the following steps:…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…The SVR has good generalization capability than many other ML algorithms (Malik et al, 2020d;Panahi et al, 2020). It is also highly robust to outliers (Borji et al, 2016;Qasem et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test

Malik

Tikhamarine

Al‐Ansari

et al. 2021

Engineering Applications of Computational Fluid Mechanics

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“…The main reason that the ANN may not be able to capture extreme values is the unavailability of the high number of extreme values in the training data, and hence the ANN cannot adequately learn the process in respect of extremes (Adnan et al, 2019a). The other main reason may be the fact that the ranges of extreme values in the training data are lower than those of the validation and/or test data (Adnan et al, 2019a;Malik et al, 2020). This leads to extrapolation difficulties in machine learning models (Kisi and Aytek, 2013;Kisi and Parmar, 2016).…”

Section: Comparative Performance Of Machine Learning Modelsmentioning

confidence: 99%

“…The test results showed that the LSSVM and MARS-based models provide more accurate prediction results compared to OP-ELM and M5Tree models. Malik et al (2020) optimized SVM by six meta-heuristic algorithms to predict daily streamflow in Naula watershed, India. They reported that SVM optimized by Harris Hawks optimization algorithm showed superior performance to the other optimized SVM models.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning Models to Predict Maximum Event Water Fractions in Streamflow

2021

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Estimating the maximum event water fraction, at which the event water contribution to streamflow reaches its peak value during a precipitation event, gives insight into runoff generation mechanisms and hydrological response characteristics of a catchment. Stable isotopes of water are ideal tracers for accurate estimation of maximum event water fractions using isotopic hydrograph separation techniques. However, sampling and measuring of stable isotopes of water is laborious, cost intensive, and often not conceivable under difficult spatiotemporal conditions. Therefore, there is a need for a proper predictive model to predict maximum event water fractions even at times when no direct sampling and measurements of stable isotopes of water are available. The behavior of maximum event water fraction at the event scale is highly dynamic and its relationships with the catchment drivers are complex and non-linear. In last two decades, machine learning algorithms have become increasingly popular in the various branches of hydrology due to their ability to represent complex and non-linear systems without any a priori assumption about the structure of the data and knowledge about the underlying physical processes. Despite advantages of machine learning, its potential in the field of isotope hydrology has rarely been investigated. Present study investigates the applicability of Artificial Neural Network (ANN) and Support Vector Machine (SVM) algorithms to predict maximum event water fractions in streamflow using precipitation, soil moisture, and air temperature as a set of explanatory input features that are more straightforward and less expensive to measure compared to stable isotopes of water, in the Schwingbach Environmental Observatory (SEO), Germany. The influence of hyperparameter configurations on the model performance and the comparison of prediction performance between optimized ANN and optimized SVM are further investigated in this study. The performances of the models are evaluated using mean absolute error (MAE), root mean squared error (RMSE), coefficient of determination (R2), and Nash-Sutcliffe Efficiency (NSE). For the ANN, the results showed that an appropriate number of hidden nodes and a proper activation function enhanced the model performance, whereas changes of the learning rate did not have a major impact on the model performance. For the SVM, Polynomial kernel achieved the best performance, whereas Linear yielded the weakest performance among the kernel functions. The result showed that maximum event water fraction could be successfully predicted using only precipitation, soil moisture, and air temperature. The optimized ANN showed a satisfactory prediction performance with MAE of 10.27%, RMSE of 12.91%, R2 of 0.70, and NSE of 0.63. The optimized SVM was superior to that of ANN with MAE of 7.89%, RMSE of 9.43%, R2 of 0.83, and NSE of 0.78. SVM could better capture the dynamics of maximum event water fractions across the events and the predictions were generally closer to the corresponding observed values. ANN tended to underestimate the events with high maximum event water fractions and to overestimate the events with low maximum event water fractions. Machine learning can prove to be a promising approach to predict variables that are not always possible to be estimated due to the lack of routine measurements.

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Support vector regression optimized by meta-heuristic algorithms for daily streamflow prediction

Cited by 97 publications

References 106 publications

Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification

Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification

Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test

Application of Machine Learning Models to Predict Maximum Event Water Fractions in Streamflow

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