Evaluation of stacking and blending ensemble learning methods for estimating daily reference evapotranspiration

Wu, Tianao; Zhang, Wei; Jiao, Xiyun; Guo, Weihua; Hamoud, Yousef Alhaj

doi:10.1016/j.compag.2021.106039

Cited by 97 publications

(60 citation statements)

References 58 publications

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“…As this case study belongs to the regression category, statistical performance metrics such as mean absolute error (MAE), root mean square error (RMSE), Nash–Sutcliffe efficiency coefficient (NSE), and coefficient of determination (R 2 ) between actual and predicted ETo were used to analyse the performance of the proposed DNN model and other baseline machine learning models of this study. RMSE, the goodness of the fit metric, is the standard deviation of the discrepancy between the predicted and the actual values (Yaseen et al 2018 ; Wu et al 2021 ). As analogous to RMSE, the goodness of fit measure that does not govern the direction of the value is considered as MAE (Yaseen et al 2018 ; Wu et al 2021 ).…”

Section: Methodsmentioning

confidence: 99%

“…The tree-based ETo estimation models, such as RF (Feng et al 2017a ; Wang et al 2019 ; Karimi et al 2020 ), gradient boosting decision tree (Ponraj and Vigneswaran 2020 ), XGBoost (Fan et al 2018 ; Wu and Fan 2019 ), and light gradient boosting machine (Fan et al 2019 ) were observed to provide more stable results, faster predictions, management of large datasets, and also the capability to prevent over-fitting, in comparison to other soft computing techniques. Recently, Wu et al ( 2021 ) unveiled stacking and blending ensemble ETo models and demonstrated their supremacy over basic machine learning and empirical models in prediction precision, stability, portability, and computing cost under complete and minimal input scenarios. While the strength of the ensemble and boosting techniques have given rise to these models and highlighted their state-of-the-art impacts in various studies, their applications in the ETo modelling are very abundant in the literature.…”

Section: Introductionmentioning

confidence: 99%

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A Deep Neural Network Architecture to Model Reference Evapotranspiration Using a Single Input Meteorological Parameter

2021

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Hydro-agrological research considers the reference evapotranspiration (ETo), driven by meteorological variables, crucial for achieving precise irrigation in precision agriculture. ETo modelling based on a single meteorological parameter would be beneficial in places where the collection of climatic parameters is challenging. The aim of this research is to develop a deep neural network (DNN) architecture that predicts daily ETo with a single input parameter selected based on the feature importance (FI) score generated by the machine learning techniques, random forest (RF), and extreme gradient boosting (XGBoost). This study also investigated the potential of SHapley Additive exPlanations to interpret and validate the outcomes of the feature selection methods by assessing the contributions of each feature to the ETo prediction. These methods recommended solar radiation as a significant parameter in the datasets of three California Irrigation Management System (CIMIS) weather stations located in distinct ETo zones. Three ETo models (DNN-Ret, XGB-Ret, and RF-Ret) were built using solar radiation as the sole input, and CIMIS ETo as the output. The performance evaluation of the developed models proved that DNN-Ret outperformed XGB-Ret and RF-Ret regardless of the dataset, with coefficients of determination (R 2 ) ranging from 0.914 to 0.954 in the local scenario, with an average decrease of 8–9.5% in mean absolute error and root mean squared error, and an improvement of 2.6–2.9% in Nash–Sutcliffe efficiency and 1.7–2% increase in R 2 . The overall result analysis highlighted the efficiency of DNN-Ret in the single input parameter based ETo modelling in diverse climatic zones.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Deep Neural Network Architecture to Model Reference Evapotranspiration Using a Single Input Meteorological Parameter

2021

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“…Many weak learner instances of the algorithm are being pooled (via boosting, bagging, etc.) together to create a strong ensemble learner, with some success [55,56]. Thus, researchers need to pay more attention to integration learning.…”

Section: Performance Of the Modelsmentioning

confidence: 99%

A Machine Learning Model for Photorespiration Response to Multi-Factors

Zheng

Bao

et al. 2021

Horticulturae

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Photorespiration results in a large amount of leaf photosynthesis consumption. However, there are few studies on the response of photorespiration to multi-factors. In this study, a machine learning model for the photorespiration rate of cucumber leaves’ response to multi-factors was established. It provides a theoretical basis for studies related to photorespiration. Machine learning models of different methods were designed and compared. The photorespiration rate was expressed as the difference between the photosynthetic rate at 2% O2 and 21% O2 concentrations. The results show that the XGBoost models had the best fit performance with an explained variance score of 0.970 for both photosynthetic rate datasets measured using air and 2% O2, with mean absolute errors of 0.327 and 0.181, root mean square errors of 1.607 and 1.469, respectively, and coefficients of determination of 0.970 for both. In addition, this study indicates the importance of the features of temperature, humidity and the physiological status of the leaves for predicted results of photorespiration. The model established in this study performed well, with high accuracy and generalization ability. As a preferable exploration of the research on photorespiration rate simulation, it has theoretical significance and application prospects.

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“…In this study, we consider two ensemble methods, blending and stacking. The idea of blending [81] is to combine different ML algorithms and use a majority vote,…”

Section: ) Ensemble Learningmentioning

confidence: 99%

“…or the average predicted probabilities in case of classification to predict the final outcome. On the other hand, the stacking method [81], [82] treats the outputs of the base estimators as input to train a second-level model on top of a selected meta estimator. Ensemble learning usually demonstrates superior performance than single models, because the strategy of model aggregation has the potential of finding more distinguishable patterns, which may not be seen by a single model.…”

Section: ) Ensemble Learningmentioning

confidence: 99%

A Machine Learning-Aided Framework to Predict Outcomes of Anti-PD-1 Therapy for Patients With Gynecological Cancer on Incomplete Post-Marketing Surveillance Dataset

et al. 2021

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Post-marketing surveillance of antineoplastic agents is performed to evaluate the efficacy and safety in patients aiming at expanding drug indications and discovering potential adverse events. The real-world data is fraught with missing values. Literature addressing different strategies for dealing with missing data in such a situation is scarce. Using machine learning (ML) algorithms for predicting therapeutic outcomes of PD-1/PD-L1 Inhibitors has attracted attention. However, training a predictive model usually requires imaging or biomarker information, which is rarely available in the post-marketing surveillance data. To address these challenges, we propose a ML-aided framework to predict the outcomes of Anti-PD-1 therapy for gynecological malignancy on a dataset with 117 patient samples, treated by Camrelizumab (with 50 patient samples), Sintilimab (44), and Toripalimab (23). Four therapeutic outcomes, including Response Evaluation Criteria in Solid Tumours (RECIST), organ adverse effect (AE), general AE, and death, are predicted. The proposed framework feeds the dataset into a learning pipeline consisting of imputation, feature engineering, model training, ensemble learning, and model selection to generate the final predictive model. We conduct experiments to justify several critical design choices, such as the specific feature engineering strategies and the SMOTE over-sampling technique. The final model for each learning task is selected from a large pool of model candidates based on a joint consideration of accuracy and F1. Moreover, we conduct thorough and visualized model analysis and gain a deeper understanding of model behavior and feature importance. The results, analysis, and findings demonstrate the superiority of the proposed learningaided framework.

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Evaluation of stacking and blending ensemble learning methods for estimating daily reference evapotranspiration

Cited by 97 publications

References 58 publications

A Deep Neural Network Architecture to Model Reference Evapotranspiration Using a Single Input Meteorological Parameter

A Deep Neural Network Architecture to Model Reference Evapotranspiration Using a Single Input Meteorological Parameter

A Machine Learning Model for Photorespiration Response to Multi-Factors

A Machine Learning-Aided Framework to Predict Outcomes of Anti-PD-1 Therapy for Patients With Gynecological Cancer on Incomplete Post-Marketing Surveillance Dataset

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