Determining the contribution of environmental factors in controlling dust pollution during cold and warm months of western Iran using different data mining algorithms and game theory

Ebrahimi-Khusfi, Zohre; Taghizadeh–Mehrjardi, Ruhollah; Roustaei, Fatemeh; Ebrahimi-Khusfi, Mohsen; Mosavi, Amir; Heung, Brandon; Soleimani-Sardo, Mojtaba; Scholten, Thomas

doi:10.1016/j.ecolind.2021.108287

Cited by 16 publications

(5 citation statements)

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“…Additionally, the squared correlation coefficient (

) between the estimated and experimental datasets, as well as the correlation coefficient (

) between the experimental and estimated values, must approach one [ 66 , 67 , 68 ]. As seen in Table 6 , the suggested MEP model meets nearly all the stated requirements, which is consistent with the findings of existing literature and recommendations [ 69 , 70 , 71 , 72 ].…”

Section: Resultssupporting

confidence: 87%

Forecasting Compressive Strength of RHA Based Concrete Using Multi-Expression Programming

et al. 2022

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Rice husk ash (RHA) is a significant pollutant produced by agricultural sectors that cause a malignant outcome to the environment. To encourage the re-use of RHA, this work used multi expression programming (MEP) to construct an empirical model for forecasting the compressive nature of concrete made with RHA (CRHA) as a cement substitute. Thus, the compressive strength of CRHA was developed comprising of 192 findings from the broad and trustworthy database obtained from literature review. The most significant characteristics, namely the specimen’s age, the percentage of RHA, the amount of cement, superplasticizer, aggregates, and the amount of water, were used as input for the modeling of CRHA. External validation, sensitivity analysis, statistical checks, and Shapley Additive Explanations (SHAP) analysis were used to evaluate the models’ performance. It was discovered that the most significant factors impacting the compressive strength of CRHA are the age of the concrete sample (AS), the amount of cement (C) and the amount of aggregate (A). The findings of this study have the potential to increase the re-use of RHA in the production of green concrete, hence promoting environmental protection and financial gain.

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“…Additionally, the squared correlation coefficient (

) between the estimated and experimental datasets, as well as the correlation coefficient (

Section: Resultssupporting

confidence: 87%

Forecasting Compressive Strength of RHA Based Concrete Using Multi-Expression Programming

et al. 2022

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“…Recent research has demonstrated that certain climate occurrences, such as drought episodes and the risks they pose, can be accurately predicted using machine learning algorithms [62]. In several scientific fields, machine learning techniques are now widely used, including: flood prediction and evaluation [63]; determining dust pollution [64]; modelling soil and landscapes [65]; and landslide susceptibility valuation [66]. Machine learning models outperform conventional statistical techniques, according to earlier studies.…”

Section: Discussionmentioning

confidence: 99%

Forecasting of SPI and Meteorological Drought Based on the Artificial Neural Network and M5P Model Tree

Pande

Al‐Ansari

Kushwaha

et al. 2022

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Climate change has caused droughts to increase in frequency and severity worldwide, which has attracted scientists to create drought prediction models to mitigate the impacts of droughts. One of the most important challenges in addressing droughts is developing accurate models to predict their discrete characteristics, i.e., occurrence, duration, and severity. The current research examined the performance of several different machine learning models, including Artificial Neural Network (ANN) and M5P Tree in forecasting the most widely used drought measure, the Standardized Precipitation Index (SPI), at both discrete time scales (SPI 3, SPI 6). The drought model was developed utilizing rainfall data from two stations in India (i.e., Angangaon and Dahalewadi) for 2000–2019, wherein the first 14 years are employed for model training, while the remaining six years are employed for model validation. The subset regression analysis was performed on 12 different input combinations to choose the best input combination for SPI 3 and SPI 6. The sensitivity analysis was carried out on the given best input combination to find the most effective parameter for forecasting. The performance of all the developed models for ANN (4, 5), ANN (5, 6), ANN (6, 7), and M5P models was assessed through the different statistical indicators, namely, MAE, RMSE, RAE, RRSE, and r. The results revealed that SPI (t-1) is the most sensitive parameters with highest values of β = 0.916, 1.017, respectively, for SPI-3 and SPI-6 prediction at both stations on the best input combinations i.e., combination 7 (SPI-1/SPI-3/SPI-4/SPI-5/SPI-8/SPI-9/SPI-11) and combination 4 (SPI-1/SPI-2/SPI-6/SPI-7) based on the higher values of R2 and Adjusted R2 while the lowest values of MSE values. It is clear from the performance of models that the M5P model has higher r values and lesser RMSE values as compared to ANN (4, 5), ANN (5, 6), and ANN (6, 7) models. Therefore, the M5P model was superior to other developed models at both stations.

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“…Moreover, as it was not feasible to consider every involved parameter in the model development, the applicability of the proposed models in special cases is questionable. Considering all the pros and cons of the proposed capacity prediction models, future researchers are recommended to work on the best AI algorithm, either individual or ensembled [ 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 ], that considers all the possible aspects and explains the mechanism involved.…”

Section: Discussionmentioning

confidence: 99%

Predicting the Lateral Load Carrying Capacity of Reinforced Concrete Rectangular Columns: Gene Expression Programming

et al. 2022

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This research presents a novel approach of artificial intelligence (AI) based gene expression programming (GEP) for predicting the lateral load carrying capacity of RC rectangular columns when subjected to earthquake loading. To achieve the desired research objective, an experimental database assembled by the Pacific Earthquake Engineering Research (PEER) center consisting of 250 cyclic tested samples of RC rectangular columns was employed. Seven input variables of these column samples were utilized to develop the coveted analytical models against the established capacity outputs. The selection of these input variables was based on the linear regression and cosine amplitude method. Based on the GEP modelling results, two analytical models were proposed for computing the flexural and shear capacity of RC rectangular columns. The performance of both these models was evaluated based on the four key fitness indicators, i.e., coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE), and root relative squared error (RRSE). From the performance evaluation results of these models, R2, RMSE, MAE, and RRSE were found to be 0.96, 53.41, 38.12, and 0.20, respectively, for the flexural capacity model, and 0.95, 39.47, 28.77, and 0.22, respectively, for the shear capacity model. In addition to these fitness criteria, the performance of the proposed models was also assessed by making a comparison with the American design code of concrete structures ACI 318-19. The ACI model reported R2, RMSE, MAE, and RRSE to be 0.88, 101.86, 51.74, and 0.39, respectively, for flexural capacity, and 0.87, 238.74, 183.66, and 1.35, respectively, for shear capacity outputs. The comparison depicted a better performance and higher accuracy of the proposed models as compared to that of ACI 318-19.

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Determining the contribution of environmental factors in controlling dust pollution during cold and warm months of western Iran using different data mining algorithms and game theory

Cited by 16 publications

References 91 publications

Forecasting Compressive Strength of RHA Based Concrete Using Multi-Expression Programming

Forecasting Compressive Strength of RHA Based Concrete Using Multi-Expression Programming

Forecasting of SPI and Meteorological Drought Based on the Artificial Neural Network and M5P Model Tree

Predicting the Lateral Load Carrying Capacity of Reinforced Concrete Rectangular Columns: Gene Expression Programming

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