Developing a hybrid model of Jaya algorithm-based extreme gradient boosting machine to estimate blast-induced ground vibrations

Zhou, Jian; Qiu, Yingui; Khandelwal, Manoj; Zhu, Shuangli; Zhang, Xiliang

doi:10.1016/j.ijrmms.2021.104856

Cited by 130 publications

(29 citation statements)

References 57 publications

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“…That is, each predictor observes a portion of the data set and must build its model based on the same portion of the data provided (i.e., the entire database is not given to each of the predictors) [17]. The Bagging tree stands for Bootstrap aggregating (Bagging) [18,19], which is described in this section. The Bagging algorithm consists of a set of basic models and operates in the following order [20].…”

Section: Methodsmentioning

confidence: 99%

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: Methodsmentioning

confidence: 99%

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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“…Application of artificial intelligence is one of the main techniques for planning and management of the research concepts (Zhou et al 2016(Zhou et al , 2021a(Zhou et al , 2021b(Zhou et al , 2021cLi et al 2017;Deng et al 2019Deng et al , 2021Guo et al 2021;Hou et al 2021). MRIO model was first proposed by economist Leontief in the 1930s, which mainly reflects the relationship between various sectors (industries) of the economic system (Nulkar 2020).…”

Section: Methodsmentioning

confidence: 99%

The relationships and trends of interregional virtual water trade based on an MRIO model

Mingfang

Bai

2021

Water Supply

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Aiming at the problem of uneven distribution of water resources, a study on the relationship and trend of virtual water trade between regions based on the MRIO model is proposed. Based on the inter-regional input-output model, the spatial distribution and changing trends of China's inter-regional virtual water trade from 2014 to 2019 are evaluated. The empirical results show that, due to the difference in water use efficiency and production processes in different departments, the direct and indirect water use coefficients and pull coefficients are different in different regions. In some water-poor areas (regions with low water availability), more products with high water use coefficients are exported, while more water-rich areas (regions with high water availability) export products with lower water use coefficients. The inter-regional virtual water trade only alleviates the shortage of water resources in some poor water areas in China. Some poor water areas export a large amount of virtual water, and the rich water areas import a large amount of virtual water or only maintain a partial virtual water balance. However, the pulling coefficient tends to increase, and the trade adjustment trends in different regions and changes in regional virtual water trade relations are not conducive to alleviating the shortage of water resources caused by uneven distribution. The tension coefficients between the food manufacturing and tobacco processing industry, textile and clothing industry, wood processing and furniture manufacturing industries in the Northeast region are relatively large, with the pulling force coefficients being 92.9, 65.5 and 156.7 respectively. Relatively speaking, the tensile coefficients of areas rich in natural water resources such as the eastern coastal, southern coastal, southwest, and central areas are generally low. According to the production situation, the regional virtual water trade relationship should be adjusted from the height of the national master plan.

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“…Artificial intelligence (AI) and machine learning (ML) techniques have been successfully implemented in the area of engineering and sciences [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32] for the last 25 years. The same models were used to solve the slope stability problems [3,11,[33][34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Slope Stability Classification under Seismic Conditions Using Several Tree-Based Intelligent Techniques

et al. 2022

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Slope stability analysis allows engineers to pinpoint risky areas, study trigger mechanisms for slope failures, and design slopes with optimal safety and reliability. Before the widespread usage of computers, slope stability analysis was conducted through semi analytical methods, or stability charts. Presently, engineers have developed many computational tools to perform slope stability analysis more efficiently. The challenge associated with furthering slope stability methods is to create a reliable design solution to perform reliable estimations involving a number of geometric and mechanical variables. The objective of this study was to investigate the application of tree-based models, including decision tree (DT), random forest (RF), and AdaBoost, in slope stability classification under seismic loading conditions. The input variables used in the modelling were slope height, slope inclination, cohesion, friction angle, and peak ground acceleration to classify safe slopes and unsafe slopes. The training data for the developed computational intelligence models resulted from a series of slope stability analyses performed using a standard geotechnical engineering software commonly used in geotechnical engineering practice. Upon construction of the tree-based models, the model assessment was performed through the use and calculation of accuracy, F1-score, recall, and precision indices. All tree-based models could efficiently classify the slope stability status, with the AdaBoost model providing the highest performance for the classification of slope stability for both model development and model assessment parts. The proposed AdaBoost model can be used as a screening tool during the stage of feasibility studies of related infrastructure projects, to classify slopes according to their expected status of stability under seismic loading conditions.

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Developing a hybrid model of Jaya algorithm-based extreme gradient boosting machine to estimate blast-induced ground vibrations

Cited by 130 publications

References 57 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

The relationships and trends of interregional virtual water trade based on an MRIO model

Slope Stability Classification under Seismic Conditions Using Several Tree-Based Intelligent Techniques

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