Applying an Efficient AI Approach for the Prediction of Bearing Capacity of Shallow Foundations

Khaleel, Faidhalrahman; Hameed, Mohammed Majeed; Khaleel, Deiaaldeen; AlOmar, Mohamed Khalid

doi:10.1007/978-3-030-97255-4_23

Cited by 5 publications

(1 citation statement)

References 26 publications

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“…Machine learning (ML) has made tremendous progress in recent years in solving numerous engineering in general [27][28][29][30][31][32] and PM 2.5 concentration in particular [33][34][35][36][37][38][39][40][41][42]. ML combines data science, statistics, and computing in an interdisciplinary fashion.…”

Section: Previous Workmentioning

confidence: 99%

The Influence of Data Length on the Performance of Artificial Intelligence Models in Predicting Air Pollution

AlOmar

Khaleel

Al-Saadi

et al. 2022

Advances in Meteorology

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Air pollution is one of humanity's most critical environmental issues and is considered contentious in several countries worldwide. As a result, accurate prediction is critical in human health management and government decision-making for environmental management. In this study, three artificial intelligence (AI) approaches, namely group method of data handling neural network (GMDHNN), extreme learning machine (ELM), and gradient boosting regression (GBR) tree, are used to predict the hourly concentration of PM2.5 over a Dorset station located in Canada. The investigation has been performed to quantify the effect of data length on the AI modeling performance. Accordingly, nine different ratios (50/50, 55/45, 60/40, 65/35, 70/30, 75/25, 80/20, 85/15, and 90/10) are employed to split the data into training and testing datasets for assessing the performance of applied models. The results showed that the data division significantly impacted the model's capacity, and the 60/40 ratio was found more suitable for developing predictive models. Furthermore, the results showed that the ELM model provides more precise predictions of PM2.5 concentrations than the other models. Also, a vital feature of the ELM model is its ability to adapt to the potential changes in training and testing data ratio. To summarize, the results reported in this study demonstrated an efficient method for selecting the optimal dataset ratios and the best AI model to predict properly which would be helpful in the design of an accurate model for solving different environmental issues.

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