Author
Tiyasha Tiyasha
Bio: Tiyasha Tiyasha is an academic researcher from Ton Duc Thang University. The author has contributed to research in topics: Computer science & Machine learning. The author has an hindex of 5, co-authored 8 publications receiving 58 citations.
Papers
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TL;DR: The proposed hybrid AI models provided a reliable and robust computer aid technology for sediment Pb prediction that contribute to the best knowledge of environmental pollution monitoring and assessment.
68 citations
TL;DR: In this article , the authors provide a clear understanding of the state-of-the-art ML models implemented for GWL modeling and the milestones achieved in this domain, as well as recommendations for possible future research directions to improve the accuracy of GWL prediction models and enhance the related knowledge.
58 citations
TL;DR: The XGBoost model validated against a diversity of data-driven models such as multilinear regression (MLR), support vector machine (SVM), and random forest (RF) and outperforms D2EHPA, EDTA, H2SO4, and NaCl predictors in order.
47 citations
TL;DR: In this paper, the reliability of four feature selector algorithms (i.e., Boruta, GA, multivariate adaptive regression splines (MARS), and extreme gradient boosting (XGBoost) to select the best suited predictor of the applied water quality (WQ) parameters was evaluated.
40 citations
TL;DR: The applied statistical analysis of the results indicates that ANN and Grid-RF models can be employed as a computer-aided model for monitoring and simulating the adsorption from aqueous solutions by Attapulgite clay.
31 citations
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TL;DR: In this paper, the authors investigated the seepage through earth-fill dams using physical, mathematical, and numerical models, and the results revealed that both mathematical calculations using L. Casagrande solutions and the SEEP/W numerical model have a plotted SEepage line compatible with the observed SEEPage line in the physical model.
Abstract: Earth-fill dams are the most common types of dam and the most economical choice. However, they are more vulnerable to internal erosion and piping due to seepage problems that are the main causes of dam failure. In this study, the seepage through earth-fill dams was investigated using physical, mathematical, and numerical models. Results from the three methods revealed that both mathematical calculations using L. Casagrande solutions and the SEEP/W numerical model have a plotted seepage line compatible with the observed seepage line in the physical model. However, when the seepage flow intersected the downstream slope and when piping took place, the use of SEEP/W to calculate the flow rate became useless as it was unable to calculate the volume of water flow in pipes. This was revealed by the big difference in results between physical and numerical models in the first physical model, while the results were compatible in the second physical model when the seepage line stayed within the body of the dam and low compacted soil was adopted. Seepage analysis for seven different configurations of an earth-fill dam was conducted using the SEEP/W model at normal and maximum water levels to find the most appropriate configuration among them. The seven dam configurations consisted of four homogenous dams and three zoned dams. Seepage analysis revealed that if sufficient quantity of silty sand soil is available around the proposed dam location, a homogenous earth-fill dam with a medium drain length of 0.5 m thickness is the best design configuration. Otherwise, a zoned earth-fill dam with a central core and 1:0.5 Horizontal to Vertical ratio (H:V) is preferred.
219 citations
TL;DR: The development of computer aid models for heavy metals (HMs) simulation has been remarkably advanced over the past two decades as mentioned in this paper, and several machine learning (ML) models have been developed for modeling HMs with outstanding progress.
128 citations
TL;DR: A deep learning (DL) based model is proposed for predicting groundwater quality and compared with three other machine learning (ML) models, namely, random forest, eXtreme gradient boosting (XGBoost), and artificial neural network, which showed that DL model is the best prediction model with the highest accuracy.
104 citations