Heavy metal contamination prediction using ensemble model: Case study of Bay sedimentation, Australia

Bhagat, Suraj Kumar; Tung, Tran Minh; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher

doi:10.1016/j.jhazmat.2020.123492

Cited by 86 publications

(21 citation statements)

References 105 publications

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“…Therefore, automatic segmentation methods could also be used in conjunction with our model to obtain improved segment interests. The ensemble or hybrid effect of algorithms can also improve prediction performance [43], [44]. Therefore, the development of these approaches has the potential for further research.…”

Section: Discussionmentioning

confidence: 99%

Deep User Segment Interest Network Modeling for Click-Through Rate Prediction of Online Advertising

2021

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Online advertising is becoming an important direction in the advertising industry with its strengths in diverse users, strong interactions, real-time feedback, and expandability. Online advertisement (Ads) can show great marketing ability by processing data from multiple channels to convey information, understanding what users want, and approaching them easily. Moreover, predicting the click-through rate (CTR) can increase advertisement revenue and user satisfaction. However, advertising data contains many features, and the amount is growing rapidly. This can be alleviated through the segmentation of users with similar interests. We assumed that the change of interest of a user could be predicted by other users' change of interest. More specifically, similar users will change their interest in a similar direction. On the basis of this idea, we proposed a novel model, the Deep User Segment Interest Network, to improve CTR prediction. We suggested three novel layers for improving performance: i) an individual interest extractor, ii) a segment interest extractor, and iii) a segment interest activation. These layers captures the latent interest of each user and creates the expressive interest representation of the segment by aggregating each user's interest. We conducted experiments using TaoBao data, which are a kind of real commercial data from an advertising platform, to confirm the CTR prediction improvement by reflecting the segment interest. The proposed algorithm obtained an AUC gain of 0.0029 with a behavior sequence length of 100. This performance exhibited the greatest improvement over other baselines, indicating the proposed method's potential contribution to business improvement.

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Section: Discussionmentioning

confidence: 99%

Deep User Segment Interest Network Modeling for Click-Through Rate Prediction of Online Advertising

2021

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“…For years, the ANN method has been recognized as a reliable tool with a mathematical structure for data processing and mimics the biological processes and neural power of the human brain (Bhagat et al 2020). Artificial neural networks were first introduced by (Rosenblatt 1958) as perceptron networks.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Solving the pan evaporation process complexity using the development of multiple mode of neurocomputing models

Ghorbani

Jabehdar

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

et al. 2021

Theor Appl Climatol

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Finding an accurate computational method for predicting pan evaporation (EP), can be useful in the application of these methods for the development of sustainable agricultural systems and water resources management. In the present study, the proposed hybrid method called Multiple Model-Support Vector Machine (MM-SVM) with the aim of increasing the accuracy of EP prediction on a monthly scale (EPm) in two meteorological stations (Ardabil and Khalkhal) using the output of artificial intelligence (AI) models (i.e., artificial neural network (ANN) and support vector machine (SVM)) were evaluated. The results of intelligent models using several statistical indices (i.e., root mean square error (RMSE), mean absolute error value (MAE), Kling-Gupta (KGE) and coefficient of determination (R 2 )) and with the help of case visual indicators Were compared. According to the results of evaluation indicators in the test phase, two models MM-SVM-6 and ANN-5 with (RMSE, MAE, KGE and R 2 equal to 1.088, 0.761, 0.79, 0.54 mm. month -1 , 0.819, 0.903 and 0.939, 0.962) and with three input variables, were introduced as the top models in Ardabil and Khalkhal stations, respectively. The proposed hybrid model (MM-SVM) was able to use its multi-model strategy with inputs predicted by independent models, its power to predict EPm in scenarios where there is a high correlation between its components with EPm, in a feasible state Accept to show. So that the incremental, constant and decreasing modes in EPm prediction accuracy by this hybrid model under the above conditions (especially in Ardabil station) were quite clear. Therefore, the results of the proposed and superior models in the present study can help local stakeholders in discussing water resources management.

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“…Complexity in heavy metal modelling tends to perform better using hybrid methods such as Convolutional Neural Networks (CNN) [125] and ensemble algorithms. Bhagat, et al [123] demonstrated the use of the XGBoost model gives higher predictability with less declination. Another popular element of hybridization is wavelet neural networks (WNN).…”

Section: ) Heavy Metal Prediction Modelsmentioning

confidence: 99%

Water, Soil and Air Pollutants’ Interaction on Mangrove Ecosystem and Corresponding Artificial Intelligence Techniques Used in Decision Support Systems - A Review

et al. 2021

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The feasibility of artificial intelligence (AI) as a predictive model for thorough efficacy analysis on environmental pollution applied on mangrove forests are discussed. Mangrove forests are among the most productive and biological diverse ecosystems on the planet. However, due to environmental pollution and climate change, mangrove forests are in serious decline. Despite crucial issues pertaining mangrove forests, the law enforcement on the ecosystem is still dubious due to the lack of evidence and data that could provide accurate analysis and prediction. The main highlight of this review elaborates on pollutant markers in soil, water, and air, by correlating these three aspects to the sustainability of mangrove ecosystem. The research gap identified from this review suggests the application of an integrated environmental prediction system for practical environmental insights. A predictive model for environmental decision-making could be developed by integrating meteorological, climatological, hydrological, atmospheric, and heavy metal concentration to understand the interaction between each factor for an efficient solution of pollutant reduction scheme involving mangrove ecosystems. INDEX TERMS Mangrove estuarine; pollutant interaction; environmental quality modelling; integrated environmental decision system.

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Heavy metal contamination prediction using ensemble model: Case study of Bay sedimentation, Australia

Cited by 86 publications

References 105 publications

Deep User Segment Interest Network Modeling for Click-Through Rate Prediction of Online Advertising

Deep User Segment Interest Network Modeling for Click-Through Rate Prediction of Online Advertising

Solving the pan evaporation process complexity using the development of multiple mode of neurocomputing models

Water, Soil and Air Pollutants’ Interaction on Mangrove Ecosystem and Corresponding Artificial Intelligence Techniques Used in Decision Support Systems - A Review

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