Cattle weight estimation using active contour models and regression trees Bagging

Weber, Vanessa Aparecida Moraes; Weber, Fabricio de Lima; Oliveira, Adair da Silva; Astolfi, Gilberto; Menezes, Geazy Vilharva; Porto, João Vitor de Andrade; Rezende, Fábio Prestes Cesar; Moraes, Pedro Henrique de; Matsubara, Edson Takashi; Mateus, Rodrigo Gonçalves; Araújo, Thiago Luís Alves Campos de; Silva, Luiz Otávio Campos da; Queiroz, Eduardo Quirino Arguelho de; Abreu, U. G. P. de; Gomes, Rodrigo da Costa; Pistori, Hemerson

doi:10.1016/j.compag.2020.105804

Cited by 40 publications

(27 citation statements)

References 62 publications

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“…Using a balanced distribution of weak and hard data, which makes the data set, difficult instances are identified by out-of-bag handlers, so that when a sample is considered "hard" it is incorrectly classified by the ensemble. This hard data is always added to the next data set while easy data has little chance of getting into the dataset [20,[81][82][83]. Performance of the Bagging based hybrid models developed in this study is slightly better than other ML models such as LASSO (R2 = 0.911), Random Forest (R2 = 0.936) and SVM (R2 = 0.935) carried out by Nguyen et al [13] on Mekong River.…”

Section: Resultsmentioning

confidence: 75%

Novel Time Series Bagging Based Hybrid Models for Predicting Historical Water Levels in the Mekong Delta Region, Vietnam

Hoan¹,

Dũng²,

Thu³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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Water level predictions in the river, lake and delta play an important role in flood management. Every year Mekong River delta of Vietnam is experiencing flood due to heavy monsoon rains and high tides. Land subsidence may also aggravate flooding problems in this area. Therefore, accurate predictions of water levels in this region are very important to forewarn the people and authorities for taking timely adequate remedial measures to prevent losses of life and property. There are so many methods available to predict the water levels based on historical data but nowadays Machine Learning (ML) methods are considered the best tool for accurate prediction. In this study, we have used surface water level data of 18 water level measurement stations of the Mekong River delta from 2000 to 2018 to build novel time-series Bagging based hybrid ML models namely: Bagging (RF), Bagging (SOM) and Bagging (M5P) to predict historical water levels in the study area. Performances of the Bagging-based hybrid models were compared with Reduced Error Pruning Trees (REPT), which is a benchmark ML model. The data of 19 years period was divided into 70:30 ratio for the modeling. The data of the period 1/2000 to 5/2013 (which is about 70% of total data) was used for the training and for the period 5/2013 to 12/2018 (which is about 30% of total data) was used for testing (validating) the models. Performance of the models was evaluated using standard statistical measures: Coefficient of Determination (R2), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Results show that the performance of all the developed models is good (R2 > 0.9) for the prediction of water levels in the study area. However, the Bagging-based hybrid models are slightly better than another model such as REPT. Thus, these Bagging-based hybrid time series models can be used for predicting water levels at Mekong data.

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Section: Resultsmentioning

confidence: 75%

Novel Time Series Bagging Based Hybrid Models for Predicting Historical Water Levels in the Mekong Delta Region, Vietnam

Hoan¹,

Dũng²,

Thu³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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“…As previously mentioned, the R 2 of the model was 0.702. MSE = 0.00662 (kW h/m 3 ) 2 , MAPE = 5.74%, RSME = 0.106 kW h/m 3 , MAE = 0.0416 kW h/m 3 , MedAE = 0.0416 kW h/m 3 , and MSLE = 0.00327 (obtained from ), which are very low . These low evaluation metrics indicate that the model for UEC of WWTPs developed in this study was quite accurate.…”

Section: Resultsmentioning

confidence: 80%

“…MSE = 0.00662 (kW h/m 3 ) 2 , MAPE = 5.74%, RSME = 0.106 kW h/m 3 , MAE = 0.0416 kW h/m 3 , MedAE = 0.0416 kW h/m 3 , and MSLE = 0.00327 (obtained from 1), which are very low. 48 These low evaluation metrics indicate that the model for UEC of WWTPs developed in this study was quite accurate. As shown in Figure 9 and Figure S2, the actual and predicted UECs exhibit the same trend when the UEC is not too high or too low.…”

Section: While Thementioning

confidence: 99%

Novel Machine Learning-Based Energy Consumption Model of Wastewater Treatment Plants

2021

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Wastewater treatment plants (WWTPs) can account for up to 1% of a country’s energy consumption. Meanwhile, WWTPs have high energy-saving potential. To achieve this, it is necessary to establish appropriate energy consumption models for WWTPs. Several recent models have been developed using logarithmic, exponential, or linear functions. However, the behavior of WWTPs is non-linear and difficult to fit with simple functions, particularly for non-numerical variables. Thus, traditional modeling methods cannot effectively describe the relationship between water and energy in WWTPs. Therefore, a machine learning method was adopted in this study to investigate the energy consumption in WWTPs; a novel energy consumption model with a non-numerical variable (discharge standard) for WWTPs was developed using the random forest algorithm. The model can also predict the energy consumption of WWTPs after upgrading discharge standards. We found that the unit electricity consumption of WWTPs exhibited an average increase of 17% after the effluent discharge standard was increased from class I B to class I A (as per China’s classification). The correlation coefficient of the model was 0.702. Thus, the developed model can provide a better understanding of energy efficiency in WWTPs.

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“…Bootstrap aggregating (Ba) is employed to enlarge inconsistency/instability extents as well as classification plots. Bagging has demonstrated by evidence or argument to be true or existing to be very sensitive to highlight the variations in training data that is contributory to boost the categorization precision of incipient intention tree classifier by decreasing variance of categorization wrong (Weber et al 2020). The points to be made about the Ba algorithm note that the punctuality of the single ML algorithm is not high, so the principal ML algorithm is repeated several times to enhancement the prediction precision, as well as the final precision of the model using the results.…”

Section: Baggingmentioning

confidence: 99%

Exploring novel hybrid soft computing models for landslide susceptibility mapping in Son La hydropower reservoir basin

Dũng

Hiếu

Phong

et al. 2021

Geomatics, Natural Hazards and Risk

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In this study, two novel hybrid models namely Bagging-based Rough Set (BRS) and AdaBoost-based Rough Set (ABRS) were used to generate landslide susceptibility maps of Son La hydropower reservoir basin, Vietnam. In total, 186 past landslide events and twelve landslides affecting factors (slope degree, slope aspect, elevation, curvature, focal flow, river density, rainfall, aquifer, weathering crust, lithology, fault density and road density) were considered in the modeling study. The landslide data was split into training (70%) and testing (30%) for the model's development and validation. One R feature selection method was used to select and prioritize the landslide affecting factors based on their importance in model prediction. Performance of the hybrid developed models was evaluated and also compared with single rough set (RS) and support vector machine (SVM) models using various standard statistical measures including area under the curve (AUC)-receiver operating characteristics (ROC) curve. The results show that the developed hybrid model BRS (AUC ¼ 0.845) is the most accurate model in comparison to other models (ABRS, SVM and RS) in predicting landslide susceptibility. Therefore, the BRS model can be used as an effective tool in the development of an accurate landslide susceptibility map of the hilly area.

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Cattle weight estimation using active contour models and regression trees Bagging

Cited by 40 publications

References 62 publications

Novel Time Series Bagging Based Hybrid Models for Predicting Historical Water Levels in the Mekong Delta Region, Vietnam

Novel Time Series Bagging Based Hybrid Models for Predicting Historical Water Levels in the Mekong Delta Region, Vietnam

Novel Machine Learning-Based Energy Consumption Model of Wastewater Treatment Plants

Exploring novel hybrid soft computing models for landslide susceptibility mapping in Son La hydropower reservoir basin

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