Prediction of heavy metals removal by polymer inclusion membranes using machine learning techniques

Yaqub, Muhammad; Eren, Beytullah; Eyüpoğlu, Volkan

doi:10.1111/wej.12699

Cited by 11 publications

(1 citation statement)

References 27 publications

(29 reference statements)

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“…Using a subset of the input dataset, MLPNN can determine the optimal mathematical relationship for predicting an outcome. Then, using the statistical values, the generated error can be calculated [42]. The model can be trained in three stages: feed-forward, error calculation, and backward circulation.…”

Section: Multilayer Perceptron Neural Network (Mlpnn)mentioning

confidence: 99%

Forecasting Water Temperature in Cascade Reservoir Operation-Influenced River with Machine Learning Models

Jiang

Yang

et al. 2022

Water

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Water temperature (WT) is a critical control for various physical and biochemical processes in riverine systems. Although the prediction of river water temperature has been the subject of extensive research, very few studies have examined the relative importance of elements affecting WT and how to accurately estimate WT under the effects of cascaded dams. In this study, a series of potential influencing variables, such as air temperature, dew temperature, river discharge, day of year, wind speed and precipitation, were used to forecast daily river water temperature downstream of cascaded dams. First, the permutation importance of the influencing variables was ranked in six different machine learning models, including decision tree (DT), random forest (RF), gradient boosting (GB), adaptive boosting (AB), support vector regression (SVR) and multilayer perceptron neural network (MLPNN) models. The results showed that day of year (DOY) plays the most important role in each model for the prediction of WT, followed by flow and temperature, which are two commonly important factors in unregulated rivers. Then, combinations of the three most important inputs were used to develop the most parsimonious model based on the six machine learning models, where their performance was compared according to statistical metrics. The results demonstrated that GB3 and RF3 gave the most accurate forecasts for the training dataset and the test dataset, respectively. Overall, the results showed that the machine learning model could be effectively applied to predict river water temperature under the regulation of cascaded dams.

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Section: Multilayer Perceptron Neural Network (Mlpnn)mentioning

confidence: 99%