Investigating the reliability of machine learning algorithms as a sustainable tool for total suspended solid prediction

Sami, Balahaha Hadi Ziyad; Khai, Wong Jee; Sami, Balahaha Fadi Ziyad; Fai, Chow Ming; Essam, Yusuf; Ahmed, Ali Najah; El-Shafie, Ahmed

doi:10.1016/j.asej.2021.01.007

Cited by 22 publications

(9 citation statements)

References 35 publications

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“…Several ML approaches have been promoted for modelling WQ parameters. The ML models applied include ANN [10,[26][27][28], adaptive neuro-fuzzy inference system (ANFIS) [7,29,30], (SVR) [31][32][33], random forest (RF) [34,35], k-nearest neighbours (KNN) [36], Naive Bayes [37], decision tree (DT) [38,39], and extreme gradient boosting (XGB) [40]. The advantages and disadvantages of the most used ML techniques are summarised in Table 2.…”

Section: Machine Learning (Ml)mentioning

confidence: 99%

A Review of Hybrid Soft Computing and Data Pre-Processing Techniques to Forecast Freshwater Quality’s Parameters: Current Trends and Future Directions

Khudhair

Zubaidi²,

Ortega‐Martorell³

et al. 2022

Environments

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Water quality has a significant influence on human health. As a result, water quality parameter modelling is one of the most challenging problems in the water sector. Therefore, the major factor in choosing an appropriate prediction model is accuracy. This research aims to analyse hybrid techniques and pre-processing data methods in freshwater quality modelling and forecasting. Hybrid approaches have generally been seen as a potential way of improving the accuracy of water quality modelling and forecasting compared with individual models. Consequently, recent studies have focused on using hybrid models to enhance forecasting accuracy. The modelling of dissolved oxygen is receiving more attention. From a review of relevant articles, it is clear that hybrid techniques are viable and precise methods for water quality prediction. Additionally, this paper presents future research directions to help researchers predict freshwater quality variables.

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Section: Machine Learning (Ml)mentioning

confidence: 99%

A Review of Hybrid Soft Computing and Data Pre-Processing Techniques to Forecast Freshwater Quality’s Parameters: Current Trends and Future Directions

Khudhair

Zubaidi²,

Ortega‐Martorell³

et al. 2022

Environments

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“…Li et al, 2021;Gunathilake et al, 2021;Huang et al, 2021;Kilinc, 2022;Kilinc & Haznedar, 2022;Kim et al, 2022;Niu & Feng, 2021;Rahimzad et al, 2021), water quality predictions (Abba et al, 2017;W. Li et al, 2020;Sami et al, 2021;Stamenković, 2021;Tahraoui et al, 2021;Y.-F. Zhang et al, 2020), and drought prediction (Adamowski et al, 2012;Ahmadi et al, 2021;Dikshit et al, 2020;M.M.H. Khan et al, 2020;Nabipour et al, 2020;Nourani et al, 2019).…”

Section: Artificial Neural Networkunclassified

Application of hybrid machine learning models and data pre-processing to predict water level of watersheds: Recent trends and future perspective

et al. 2022

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The community's well-being and economic livelihoods are heavily influenced by the water level of watersheds. The changes in water levels directly affect the circulation processes of lakes and rivers that control water mixing and bottom sediment resuspension, further affecting water quality and aquatic ecosystems. Thus, these considerations have made the water level monitoring process essential to save the environment. Machine learning hybrid models are emerging robust tools that are successfully applied for water level monitoring. Various models have been developed, and selecting the optimal model would be a lengthy procedure. A timely, detailed, and instructive overview of the models' concepts and historical uses would be beneficial in preventing researchers from overlooking models' potential selection and saving significant time on the problem. Thus, recent research on water level prediction using hybrid machines is reviewed in this article to present the "state of the art" on the subject and provide some suggestions on research methodologies and models. This comprehensive study classifies hybrid models into four types algorithm parameter optimisation-based hybrid models (OBH), pre-processingbased hybrid models (PBH), the components combination-based hybrid models (CBH), and hybridisation of parameter optimisation-based with preprocessing-based hybrid models (HOPH); furthermore, it explains the pre-processing of data in detail. Finally, the most popular optimisation methods and future perspectives and conclusions have been discussed.

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“…The average of the squares of the errors is calculated from the average squared difference between the predicted and observed data by a predicting data indicator [39]. It does, in fact, describe the construction of the set of points along the regression line [40]. As seen in Equation 1, it does this by squaring the distances between the points and the regression line (these distances are the 'errors') (3).…”

Section: Mean Square Error Msementioning

confidence: 99%

Concrete Compressive Strength Prediction Using Machine Learning Algorithms

Turkey

Beddu

Ahmed

et al. 2022

Preprint

Self Cite

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The most widely utilized construction material is concrete. Concrete's physical qualities differ depending on the kind. In this paper, we predicted the compressive strength of four types of lightweight aggregate geopolymer concretes (LWAGC), namely, lightweigh expanded clay Leca, recycled foam masonry aggregate RFA, Porcelanite aggregate PA and recycled brick aggregate RBA. For predictions, we used seven models, specifically, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), Decision Tree (DT), Random Forest (RF), Support Vector Regressor (SVR) and Linear Regression (LR). For evaluation, we employed six metrics, Root Mean Square Error (RMSE), Mean Squared Logarithmic Error (MSLE), Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Logarithmic Error (RMSLE) and Coefficient of Determination R². The results of predictions demonstrate that RF offers high accuracy for Leca type (MSE= 2,6697; RMSE= 1,6339; MSLE= 0,0072; RMSLE= 0,0851; MAE= 1,3434; R²=0,8687), Porcelanite type (MSE= 2,9650; RMSE= 1,7219; MSLE= 0,0065; RMSLE= 0,0805; MAE= 1,5693; R²=0,8437) and RFA type (MSE= 1,7028; RMSE= 1,3049; MSLE= 0,0059; RMSLE= 0,0768; MAE= 1,1300; R²=0,8099) because the predictions are closer to the real values, and DT offers better predictions than other models for RBA type (MSE= 3,3069; RMSE= 1,8185; MSLE= 0,0066; RMSLE= 0,0812; MAE= 1,5610; R²=0,8764). İn order to test the models in predicting new value we gave them a 675° as new temperature and we found that that LR is more accurate than other models in predicting the CS of Leca, RBA and Porcelanite types. However, CNN outperformed other models in predicting RFA type.

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Investigating the reliability of machine learning algorithms as a sustainable tool for total suspended solid prediction

Cited by 22 publications

References 35 publications

A Review of Hybrid Soft Computing and Data Pre-Processing Techniques to Forecast Freshwater Quality’s Parameters: Current Trends and Future Directions

A Review of Hybrid Soft Computing and Data Pre-Processing Techniques to Forecast Freshwater Quality’s Parameters: Current Trends and Future Directions

Application of hybrid machine learning models and data pre-processing to predict water level of watersheds: Recent trends and future perspective

Concrete Compressive Strength Prediction Using Machine Learning Algorithms

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