Efficiency enhancement of a process-based rainfall–runoff model using a new modified AdaBoost.RT technique

Liu, Shuang; Xu, Jingwen; Jun, Zhao; Xie, Xingmei; Zhang, Wanchang

doi:10.1016/j.asoc.2014.05.033

Cited by 31 publications

(14 citation statements)

References 30 publications

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“…Thus, KNN‐AdaBoost which combined K ‐Nearest Neighbor (KNN) and adaptive boosting (AdaBoost) is proposed as a novel algorithm for efficient black tea samples discrimination. Likewise, the number of PCs and the number of iterations ( M ) were the two main parameters that should be optimized for guaranteeing the accuracy of this model (Liu, Xu, Zhao, Xie, & Zhang, ). Herein, 10 values of M (1–50, step 5) and 10 PCs (1–10, step 1) were selected respectively, and the optimal parameters were determined according to the highest discrimination rates.…”

Section: Resultsmentioning

confidence: 99%

Monitoring black tea fermentation using a colorimetric sensor array-based artificial olfaction system

Zhang

et al. 2017

J Food Process Preserv

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The fermentation process plays an important role in black tea's quality control and determination.This article presented a portable artificial olfaction system for real-time monitoring of black tea.Herein, a colorimetric sensor array, which was fabricated by printing 12 chemical dyes, including 9 porphyrins/metalloporphyrins and 3 pH indicators on a C2 reverse silica-get flat plate, was utilized to develop the artificial olfaction system for data acquisition. With specific colorific fingerprint of volatile compounds from black tea, fermentation time can be achieved with efficient chemometrics. Additionally, a novel classification algorithm, which combined K-Nearest Neighbor and adaptive boosting, namely KNN-AdaBoost, was proposed with a comparison of two classical classification algorithms (e.g., KNN and BP-ANN). Results demonstrated that the optimum KNNAdaBoost model was superior to others with discrimination rate of 100% in both the training and prediction set. The overall results sufficiently reveal that the colorimetric sensor has promising applications in real-time fermentation monitoring. Practical applicationsThe fermentation process plays an important role in black tea's quality control and determination.However, traditional analytical chemical methods (i.e., mass spectrometry, liquid chromatography gas, and gas chromatography-mass spectrometry) can monitor the fermentation process and have some drawbacks, such as complicated operation, high costs of implementation, and lengthy analysis time. This research notably proposed a portable artificial olfaction system for real-time monitoring black tea; and examined the feasibility, accuracy and effectiveness of this sensor. The overall results obtained from this study sufficiently reveals that the colorimetric sensor has promising applications in real-time fermentation monitoring.

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Section: Resultsmentioning

confidence: 99%

Monitoring black tea fermentation using a colorimetric sensor array-based artificial olfaction system

Zhang

et al. 2017

J Food Process Preserv

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“…In a later study, the same authors compared the performance of AdaBoosted M5 tree models against ANN models for various applications, including predicting river flows in a catchment; they found higher performance in models that used the AdaBoost.RT algorithm compared to single ANNs (Shrestha and Solomatine, 2006). Liu et al (2014) used AdaBoost.RT for calibrating process-based rainfall-runoff models, and found improved performance over the single model predictions. Wu et al…”

Section: Adaptive Boostingmentioning

confidence: 99%

Resampling and ensemble techniques for improving ANN-based high streamflow forecast accuracy

Snieder¹,

Abogadil²,

Khan³

2020

Preprint

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Abstract. Data-driven flow forecasting models, such as Artificial Neural Networks (ANNs), are increasingly used for operational flood warning systems. However, flow distributions are highly imbalanced, resulting in poor prediction accuracy on high flows, both in terms of amplitude and timing error. Resampling and ensemble techniques have shown to improve model performance of imbalanced datasets such as streamflow. In this research, we systematically evaluate and compare three resampling: random undersampling (RUS), random oversampling (ROS), and SMOTER; and four ensemble techniques: randomised weights and biases, bagging, adaptive boosting (AdaBoost), least squares boosting (LSBoost); on their ability to improve high flow prediction accuracy using ANNs. The methods are implemented both independently and in combined, hybrid techniques. While some of these combinations have been explored in the broader machine learning literature, this research contains many of the first instances of these algorithms to address the imbalance problem inherent in flood and high flow forecasting models. Specifically, the implementation of ROS, and new approaches for SMOTER, LSBOOST, and SMOTER-AdaBoost are presented in this research. Data from two Canadian watersheds (the Bow River in Alberta, and the Don River in Ontario), representing distinct hydrological systems, are used as the basis for the comparison of the methods. The models are evaluated on overall performance and on high flows. The results of this research indicate that resampling produces marginal improvements to high flow prediction accuracy, whereas ensemble methods produce more substantial improvements, with or without a resampling method. Compared to simple ANN flow forecast models, the use of ensemble methods is recommended to reduce the amplitude and timing error in highly imbalanced flow datasets.

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“…The approach incorporates self-adaptive mechanism supervised by the root mean square error trend in the iteration process to ensure the good prediction performance. Liu et al [28] developed a new modified AdaBoost.RT technique by weighting the wrong predicted sample and loss function, which is proved as an efficient way to enhance the predictive ability of XXT.…”

Section: Introductionmentioning

confidence: 99%

Temperature Compensation of Piezo-Resistive Pressure Sensor Utilizing Ensemble AMPSO-SVR Based on Improved Adaboost.RT

Zhang

et al. 2020

IEEE Access

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As the silicon material is severely influenced by the ambient temperature, the silicon piezo-resistive pressure sensor remarkably suffers from a strong nonlinearity in the response characteristic as the ambient temperature varies. To address this crucial issue, an adaptive mutation particle swarm optimization optimized support vector regression (AMPSO-SVR) combined with improved AdaBoost.RT algorithm is presented. The opposition-based learning initialization and Levy mutation is applied in the adaptive mutation particle swarm optimization (AMPSO) to achieve the appropriate model selection task which directly determines the performance of SVR. The performance of original AdaBoost.RT is improved by a dynamical modification approach for threshold and quoted error criterion. In order to verify the effectiveness of the proposed temperature compensation approach, several additional optimization methods such as Cuckoo search (CS), dragonfly algorithm (DA), multi-verse optimizer (MVO), conventional particle swarm optimization (PSO), Levy flight improved particle swarm optimization (Levy-PSO) and the AMPSO combined with SVR are investigated. The minimum quoted error, maximum quoted error, the mean quoted error and the variance of the quoted error over testing data obtained by the proposed method are 6.8764×10 −5 , 6.4463×10 −4 , 3.2619×10 −4 and 2.5714×10 −8 respectively, which are superior to the corresponding indices obtained by other methods. The analysis of simulation results indicates the method proposed in this research is applicable, effective and efficient for industrial application.

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Efficiency enhancement of a process-based rainfall–runoff model using a new modified AdaBoost.RT technique

Cited by 31 publications

References 30 publications

Monitoring black tea fermentation using a colorimetric sensor array-based artificial olfaction system

Monitoring black tea fermentation using a colorimetric sensor array-based artificial olfaction system

Resampling and ensemble techniques for improving ANN-based high streamflow forecast accuracy

Temperature Compensation of Piezo-Resistive Pressure Sensor Utilizing Ensemble AMPSO-SVR Based on Improved Adaboost.RT

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