Prediction of sediment heavy metal at the Australian Bays using newly developed hybrid artificial intelligence models

Bhagat, Suraj Kumar; Tiyasha, Tiyasha; Awadh, Salih Muhammad; Tung, Tran Minh; Jawad, Ali H.; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher

doi:10.1016/j.envpol.2020.115663

Cited by 83 publications

(23 citation statements)

References 47 publications

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“…Through the PCA operation, the initial N-dimensional matrix will be transformed into a K-dimensional matrix (K < N). The calculation processes in the PCA component reduction operation can refer to the following literature [56][57][58].…”

Section: Principal Component Analysis (Pca) Denoisingmentioning

confidence: 99%

Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China

Wang

Liu

Yue

et al. 2020

Water

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A gated recurrent unit (GRU) network, which is a kind of artificial neural network (ANN), has been increasingly applied to runoff forecasting. However, knowledge about the impact of different input data filtering strategies and the implications of different architectures on the GRU runoff forecasting model’s performance is still insufficient. This study has selected the daily rainfall and runoff data from 2007 to 2014 in the Wei River basin in Shaanxi, China, and assessed six different scenarios to explore the patterns of that impact. In the scenarios, four manually-selected rainfall or runoff data combinations and principal component analysis (PCA) denoised input have been considered along with single directional and bi-directional GRU network architectures. The performance has been evaluated from the aspect of robustness to 48 various hypermeter combinations, also, optimized accuracy in one-day-ahead (T + 1) and two-day-ahead (T + 2) forecasting for the overall forecasting process and the flood peak forecasts. The results suggest that the rainfall data can enhance the robustness of the model, especially in T + 2 forecasting. Additionally, it slightly introduces noise and affects the optimized prediction accuracy in T + 1 forecasting, but significantly improves the accuracy in T + 2 forecasting. Though with relevance (R = 0.409~0.763, Grey correlation grade >0.99), the runoff data at the adjacent tributary has an adverse effect on the robustness, but can enhance the accuracy of the flood peak forecasts with a short lead time. The models with PCA denoised input has an equivalent, even better performance on the robustness and accuracy compared with the models with the well manually filtered data; though slightly reduces the time-step robustness, the bi-directional architecture can enhance the prediction accuracy. All the scenarios provide acceptable forecasting results (NSE of 0.927~0.951 for T + 1 forecasting and 0.745~0.836 for T + 2 forecasting) when the hyperparameters have already been optimized. Based on the results, recommendations have been provided for the construction of the GRU runoff forecasting model.

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Section: Principal Component Analysis (Pca) Denoisingmentioning

confidence: 99%

Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China

Wang

Liu

Yue

et al. 2020

Water

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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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“…The principal component analysis (PCA) is a wellrecognized feature selection approach that works based on un-supervised pattern recognition. It abstracts the frequent pattern that scores the highest in the simulated matrix [67]. The mathematical procedure of the PCA approach is working on the base to allocate the minimum error between the observed and predicted values due to the variance of the principal component [68].…”

Section: F Pricipal Component Analysismentioning

confidence: 99%

Proposition of New Ensemble Data-Intelligence Models for Surface Water Quality Prediction

et al. 2021

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An accurate prediction of water quality (WQ) related parameters is considered as pivotal decisive tool in sustainable water resources management. In this study, five different ensemble machine learning (ML) models including Quantile regression forest (QRF), Random Forest (RF), radial support vector machine (SVM), Stochastic Gradient Boosting (GBM) and Gradient Boosting Machines (GBM_H2O) were developed to predict the monthly biochemical oxygen demand (BOD) values of the Euphrates River, Iraq. For this aim, monthly average data of water temperature (T), Turbidity, pH, Electrical Conductivity (EC), Alkalinity (Alk), Calcium (Ca), chemical oxygen demand (COD), Sulfate (SO4), total dissolved solids (TDS), total suspended solids (TSS), and BOD measured for ten years period were used in this study. The performances of these standalone models were compared with integrative models developed by coupling the applied ML models with two different feature extraction algorithms i.e., Genetic Algorithm (GA) and Principal Components Analysis (PCA). The reliability of the applied models was evaluated based on the statistical performance criteria of determination coefficient (R 2 ), root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe model efficiency coefficient (NSE), Willmott index (d), and percent bias (PBIAS). Results showed that among the developed models, QRF model attained the superior performance. The performance of the evaluated models presented in this study proved that the developed integrative PCA-QRF model presented much better performance compared with the standalone ones and with those integrated with GA. The statistical criteria of R 2 , RMSE, MAE, NSE, d, and PBIAS of PCA-QRF were 0.94, 0.12, 0.05, 0.93, 0.98, and 0.3, respectively.

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Prediction of sediment heavy metal at the Australian Bays using newly developed hybrid artificial intelligence models

Cited by 83 publications

References 47 publications

Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China

Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China

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

Proposition of New Ensemble Data-Intelligence Models for Surface Water Quality Prediction

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