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Mrs. Tiyasha Tiyasha
Ton Duc Thang University, Ho Chi Minh City, Vietnam

Basic Info


Research Keywords & Expertise

1 Artificial Intelligence
1 Environmental Engineering
1 Water & Wastewater Design
1 Water Testing
1 Emerging contaminants

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Journal article
Published: 14 July 2021 in Marine Pollution Bulletin
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Dissolved oxygen (DO) is an important indicator of river health for environmental engineers and ecological scientists to understand the state of river health. This study aims to evaluate the reliability of four feature selector algorithms i.e., Boruta, genetic algorithm (GA), multivariate adaptive regression splines (MARS), and extreme gradient boosting (XGBoost) to select the best suited predictor of the applied water quality (WQ) parameters; and compare four tree-based predictive models, namely, random forest (RF), conditional random forests (cForest), RANdom forest GEneRator (Ranger), and XGBoost to predict the changes of dissolved oxygen (DO) in the Klang River, Malaysia. The total features including 15 WQ parameters from monitoring site data and 7 hydrological components from remote sensing data. All predictive models performed well as per the features selected by the algorithms XGBoost and MARS in terms applied statistical evaluators. Besides, the best performance noted in case of XGBoost predictive model among all applied predictive models when the feature selected by MARS and XGBoost algorithms, with the coefficient of determination (R2) values of 0.84 and 0.85, respectively, nonetheless the marginal performance came up by Boruta-XGBoost model on in this scenario.

ACS Style

Tiyasha Tiyasha; Tran Minh Tung; Suraj Kumar Bhagat; Mou Leong Tan; Ali H. Jawad; Wan Hanna Melini Wan Mohtar; Zaher Mundher Yaseen. Functionalization of remote sensing and on-site data for simulating surface water dissolved oxygen: Development of hybrid tree-based artificial intelligence models. Marine Pollution Bulletin 2021, 170, 112639 .

AMA Style

Tiyasha Tiyasha, Tran Minh Tung, Suraj Kumar Bhagat, Mou Leong Tan, Ali H. Jawad, Wan Hanna Melini Wan Mohtar, Zaher Mundher Yaseen. Functionalization of remote sensing and on-site data for simulating surface water dissolved oxygen: Development of hybrid tree-based artificial intelligence models. Marine Pollution Bulletin. 2021; 170 ():112639.

Chicago/Turabian Style

Tiyasha Tiyasha; Tran Minh Tung; Suraj Kumar Bhagat; Mou Leong Tan; Ali H. Jawad; Wan Hanna Melini Wan Mohtar; Zaher Mundher Yaseen. 2021. "Functionalization of remote sensing and on-site data for simulating surface water dissolved oxygen: Development of hybrid tree-based artificial intelligence models." Marine Pollution Bulletin 170, no. : 112639.

Journal article
Published: 04 March 2021 in Chemosphere
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Copper (Cu) ion in wastewater is considered as one of the crucial hazardous elements to be quantified. This research is established to predict copper ions adsorption (Ad) by Attapulgite clay from aqueous solutions using computer-aided models. Three artificial intelligent (AI) models are developed for this purpose including Grid optimization-based random forest (Grid-RF), artificial neural network (ANN) and support vector machine (SVM). Principal component analysis (PCA) is used to select model inputs from different variables including the initial concentration of Cu (IC), the dosage of Attapulgite clay (Dose), contact time (CT), pH, and addition of NaNO3 (SN). The ANN model is found to predict Ad with minimum root mean square error (RMSE = 0.9283) and maximum coefficient of determination (R2 = 0.9974) when all the variables (i.e., IC, Dose, CT, pH, SN) were considered as input. The prediction accuracy of Grid-RF model is found similar to ANN model when a few numbers of predictors are used. According to prediction accuracy, the models can be arranged as ANN-M5> Grid-RF-M5> Grid-RF-M4> ANN-M4> SVM-M4> SVM-M5. Overall, 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.

ACS Style

Suraj Kumar Bhagat; Konstantina Pyrgaki; Sinan Q. Salih; Tiyasha Tiyasha; Ufuk Beyaztas; Shamsuddin Shahid; Zaher Mundher Yaseen. Prediction of copper ions adsorption by attapulgite adsorbent using tuned-artificial intelligence model. Chemosphere 2021, 276, 130162 .

AMA Style

Suraj Kumar Bhagat, Konstantina Pyrgaki, Sinan Q. Salih, Tiyasha Tiyasha, Ufuk Beyaztas, Shamsuddin Shahid, Zaher Mundher Yaseen. Prediction of copper ions adsorption by attapulgite adsorbent using tuned-artificial intelligence model. Chemosphere. 2021; 276 ():130162.

Chicago/Turabian Style

Suraj Kumar Bhagat; Konstantina Pyrgaki; Sinan Q. Salih; Tiyasha Tiyasha; Ufuk Beyaztas; Shamsuddin Shahid; Zaher Mundher Yaseen. 2021. "Prediction of copper ions adsorption by attapulgite adsorbent using tuned-artificial intelligence model." Chemosphere 276, no. : 130162.

Research article
Published: 20 February 2021 in Environmental Science and Pollution Research
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This study investigates the performance of support vector machine (SVM), multivariate adaptive regression spline (MARS), and random forest (RF) models for predicting the lead (Pb) adsorption by attapulgite clay. Models are constructed using batch stochastic data of heavy metal (HM) concentrations under different physicochemical conditions. Implementation of auto-hyper-parameter tuning using grid-search approach and comparative analysis is performed against the benchmark artificial intelligence (AI) models. Models are constructed based on Pb concentration (IC), the dosage of attapulgite clay (dose), contact time (CT), pH, and NaNO3 (SN). Principle component analysis (PCA) and correlation analysis (CA) methods are integrated to assess the importance of the applied predictors and their relationship with the target. Research findings approved the potential of the grid-RF model as a marginal superior predictive model against the grid-SVM in terms of MAE, i.e., 3.29 and 3.34, respectively; moreover, the md scored the same, i.e., 0.93, which reveals the potential predictability for both. Nonetheless, grid-MARS and standalone MARS models remained likewise in their predictability. IC parameter demonstrated the highest influential among all the predictors with the highest value of importance in the case of all three evaluators. The solution pH and dose stands together with marginal differences in case of PCA method; however, solution pH and CT appeared with similarity impact using the PCA method.

ACS Style

Suraj Kumar Bhagat; Mariapparaj Paramasivan; Mustafa Al-Mukhtar; Tiyasha Tiyasha; Konstantina Pyrgaki; Tran Minh Tung; Zaher Mundher Yaseen. Prediction of lead (Pb) adsorption on attapulgite clay using the feasibility of data intelligence models. Environmental Science and Pollution Research 2021, 1 -19.

AMA Style

Suraj Kumar Bhagat, Mariapparaj Paramasivan, Mustafa Al-Mukhtar, Tiyasha Tiyasha, Konstantina Pyrgaki, Tran Minh Tung, Zaher Mundher Yaseen. Prediction of lead (Pb) adsorption on attapulgite clay using the feasibility of data intelligence models. Environmental Science and Pollution Research. 2021; ():1-19.

Chicago/Turabian Style

Suraj Kumar Bhagat; Mariapparaj Paramasivan; Mustafa Al-Mukhtar; Tiyasha Tiyasha; Konstantina Pyrgaki; Tran Minh Tung; Zaher Mundher Yaseen. 2021. "Prediction of lead (Pb) adsorption on attapulgite clay using the feasibility of data intelligence models." Environmental Science and Pollution Research , no. : 1-19.

Journal article
Published: 10 August 2020 in Ecotoxicology and Environmental Safety
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Exploring the Manganese (Mn) removal prediction with several independent variables is tremendously critical and indispensable to understand the pattern of removal process. Mn is one of the key heavy metals (HMs) stipulated by the WHO for the development of many attributes of the ecosystem in controlled quantity. In the present paper, an extreme gradient model (XGBoost) is proposed for Mn prediction. A compressive statistical analysis reveals the stochastics behaviour of the data prior to the prediction investigation. The main goal is to determine the Mn predictability of XGBoost algorithm with influencing factors such as D2EHPA (M), Time (min), H2SO4 (M), NaCl (g/L), and EDTA (mM). The PCA biplot signifies the importance of the predictors. The XGBoost model validated against a diversity of data-driven models such as multilinear regression (MLR), support vector machine (SVM), and random forest (RF). The order of the applied models' performance are XGBoost > RF > SVM > MLR as per their R2 and RMSE metrics over testing phase i.e. 20.88, 0.75, 0.61, 0.40, and 2.23, 3.01, 3.51, 6.38, respectively. Moreover, the Taylor diagram and Radar chart have drown to emphasize the XGBoost model efficiency, stability, and reliability. In respect of XGBoost model prediction, ‘Time’ predictor outperforms D2EHPA, EDTA, H2SO4, and NaCl predictors in order.

ACS Style

Suraj Kumar Bhagat; Tiyasha Tiyasha; Tran Minh Tung; Reham R. Mostafa; Zaher Mundher Yaseen. Manganese (Mn) removal prediction using extreme gradient model. Ecotoxicology and Environmental Safety 2020, 204, 111059 .

AMA Style

Suraj Kumar Bhagat, Tiyasha Tiyasha, Tran Minh Tung, Reham R. Mostafa, Zaher Mundher Yaseen. Manganese (Mn) removal prediction using extreme gradient model. Ecotoxicology and Environmental Safety. 2020; 204 ():111059.

Chicago/Turabian Style

Suraj Kumar Bhagat; Tiyasha Tiyasha; Tran Minh Tung; Reham R. Mostafa; Zaher Mundher Yaseen. 2020. "Manganese (Mn) removal prediction using extreme gradient model." Ecotoxicology and Environmental Safety 204, no. : 111059.

Review
Published: 24 February 2020 in Sustainability
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Dam and powerhouse operation sustainability is a major concern from the hydraulic engineering perspective. Powerhouse operation is one of the main sources of vibrations in the dam structure and hydropower plant; thus, the evaluation of turbine performance at different water pressures is important for determining the sustainability of the dam body. Draft tube turbines run under high pressure and suffer from connection problems, such as vibrations and pressure fluctuation. Reducing the pressure fluctuation and minimizing the principal stress caused by undesired components of water in the draft tube turbine are ongoing problems that must be resolved. Here, we conducted a comprehensive review of studies performed on dams, powerhouses, and turbine vibration, focusing on the vibration of two turbine units: Kaplan and Francis turbine units. The survey covered several aspects of dam types (e.g., rock and concrete dams), powerhouse analysis, turbine vibrations, and the relationship between dam and hydropower plant sustainability and operation. The current review covers the related research on the fluid mechanism in turbine units of hydropower plants, providing a perspective on better control of vibrations. Thus, the risks and failures can be better managed and reduced, which in turn will reduce hydropower plant operation costs and simultaneously increase the economical sustainability. Several research gaps were found, and the literature was assessed to provide more insightful details on the studies surveyed. Numerous future research directions are recommended.

ACS Style

Zaher Mundher Yaseen; Ameen Mohammed Salih Ameen; Mohammed Suleman Aldlemy; Ameen Mohammed Salih; Haitham Abdulmohsin Afan; Senlin Zhu; Ahmed Mohammed Sami Al-Janabi; Nadhir Al-Ansari; Tiyasha Tiyasha; Hai Tao. State-of-the Art-Powerhouse, Dam Structure, and Turbine Operation and Vibrations. Sustainability 2020, 12, 1676 .

AMA Style

Zaher Mundher Yaseen, Ameen Mohammed Salih Ameen, Mohammed Suleman Aldlemy, Ameen Mohammed Salih, Haitham Abdulmohsin Afan, Senlin Zhu, Ahmed Mohammed Sami Al-Janabi, Nadhir Al-Ansari, Tiyasha Tiyasha, Hai Tao. State-of-the Art-Powerhouse, Dam Structure, and Turbine Operation and Vibrations. Sustainability. 2020; 12 (4):1676.

Chicago/Turabian Style

Zaher Mundher Yaseen; Ameen Mohammed Salih Ameen; Mohammed Suleman Aldlemy; Ameen Mohammed Salih; Haitham Abdulmohsin Afan; Senlin Zhu; Ahmed Mohammed Sami Al-Janabi; Nadhir Al-Ansari; Tiyasha Tiyasha; Hai Tao. 2020. "State-of-the Art-Powerhouse, Dam Structure, and Turbine Operation and Vibrations." Sustainability 12, no. 4: 1676.

Review article
Published: 14 February 2020 in Journal of Hydrology
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There has been an unsettling rise in the river contamination due to the climate change and anthropogenic activities. Last decades’ research has immensely focussed on river basin water quality (WQ) prediction, risk assessment and pollutant classification techniques to design more potent management policies and advanced early warning system. The next challenge is dealing with water-related data as they are problematic to handle owing to their nonlinearity, nonstationary feature and vague properties due to the unpredictable natural changes, interdependent relationship, human interference and complexity. Artificial intelligence (AI) models have shown remarkable success and superiority to handle such data owing to their higher accuracy to deal with non-linear data, robustness, reliability, cost-effectiveness, problem-solving capability, decision-making capability, efficiency and effectiveness. AI models are the perfect tools for river WQ monitoring, management, sustainability and policymaking. This research reports the state of the art of various AI models implemented for river WQ simulation over the past two decades (2000–2020). Correspondingly, over 200 research articles are reviewed from the Web of Science journals. The survey covers the model structure, input variability, performance metrics, regional generalisation investigation and comprehensive assessments of AI models progress in river water quality research. The increasing contaminants, the lack of funding and the deficiency in data, numerous variables and unique data time series pattern based on the geological area have increased the need for river WQ monitoring and control even more. Hence, this is highly emphasising the involvement of AI models development which can deal with missing data, able to integrate the features of a black-box model and white-box models, benchmarked model and automated early warning system are few of many points need more research. Despite extensive research on WQ simulation using AI models, shortcomings remain according to the current survey, and several possible future research directions are proposed. Overall, this survey provides a new milestone in water resource engineering on the AI model implementation, innovation and transformation in surface WQ modelling with many formidable problems in different blossoming area and objectives to be achieved in the future.

ACS Style

Tiyasha; Tran Minh Tung; Zaher Mundher Yaseen. A survey on river water quality modelling using artificial intelligence models: 2000–2020. Journal of Hydrology 2020, 585, 124670 .

AMA Style

Tiyasha, Tran Minh Tung, Zaher Mundher Yaseen. A survey on river water quality modelling using artificial intelligence models: 2000–2020. Journal of Hydrology. 2020; 585 ():124670.

Chicago/Turabian Style

Tiyasha; Tran Minh Tung; Zaher Mundher Yaseen. 2020. "A survey on river water quality modelling using artificial intelligence models: 2000–2020." Journal of Hydrology 585, no. : 124670.

Journal article
Published: 10 December 2019 in Scientific Reports
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Numerous researchers have expressed concern over the emerging water scarcity issues around the globe. Economic water scarcity is severe in the developing countries; thus, the use of inexpensive wastewater treatment strategies can help minimize this issue. An abundant amount of laundry wastewater (LWW) is generated daily and various wastewater treatment researches have been performed to achieve suitable techniques. This study addressed this issue by considering the economic perspective of the treatment technique through the selection of easily available materials. The proposed technique is a combination of locally available absorbent materials such as sand, biochar, and teff straw in a media. Biochar was prepared from eucalyptus wood, teff straw was derived from teff stem, and sand was obtained from indigenous crushed stones. In this study, the range of laundry wastewater flow rate was calculated as 6.23–17.58 m3/day; also studied were the efficiency of the media in terms of the removal percentage of contamination and the flux rate. The performances of biochar and teff straw were assessed based on the operation parameters and the percentage removal efficiency at different flux rates; the assessment showed 0.4 L/min flux rate to exhibit the maximum removal efficiency. Chemical oxygen demand, biological oxygen demand, and total alkalinity removal rate varied from 79% to ≥83%; total solids and total suspended solids showed 92% to ≥99% removal efficiency, while dissolved oxygen, total dissolved solids, pH, and electrical conductivity showed 22% to ≥62% removal efficiency. The optimum range of pH was evaluated between 5.8–7.1. The statistical analysis for finding the correlated matrix of laundry wastewater parameters showed the following correlations: COD (r = −0.84), TS (r = −0.83), and BOD (r = −0.81), while DO exhibited highest negative correlation. This study demonstrated the prospective of LWW treatment using inexpensive materials. The proposed treatment process involved low-cost materials and exhibited efficiency in the removal of contaminants; its operation is simple and can be reproduced in different scenarios.

ACS Style

Zaher Mundher Yaseen; Tibebu Tsegaye Zigale; Tiyasha Tiyasha; Ravi Kumar D.; Sinan Q. Salih; Suyash Awasthi; Tran Minh Tung; Nadhir Al-Ansari; Suraj Kumar Bhagat. Laundry wastewater treatment using a combination of sand filter, bio-char and teff straw media. Scientific Reports 2019, 9, 1 -11.

AMA Style

Zaher Mundher Yaseen, Tibebu Tsegaye Zigale, Tiyasha Tiyasha, Ravi Kumar D., Sinan Q. Salih, Suyash Awasthi, Tran Minh Tung, Nadhir Al-Ansari, Suraj Kumar Bhagat. Laundry wastewater treatment using a combination of sand filter, bio-char and teff straw media. Scientific Reports. 2019; 9 (1):1-11.

Chicago/Turabian Style

Zaher Mundher Yaseen; Tibebu Tsegaye Zigale; Tiyasha Tiyasha; Ravi Kumar D.; Sinan Q. Salih; Suyash Awasthi; Tran Minh Tung; Nadhir Al-Ansari; Suraj Kumar Bhagat. 2019. "Laundry wastewater treatment using a combination of sand filter, bio-char and teff straw media." Scientific Reports 9, no. 1: 1-11.

Journal article
Published: 08 October 2019 in Water
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With increasing population, the need for research ideas on the field of reducing wastage of water can save a big amount of water, money, time, and energy. Water leakage (WL) is an essential problem in the field of water supply field. This research is focused on real water loss in the water distribution system located in Ethiopia. Top-down and bursts and background estimates (BABE) methodology is performed to assess the data and the calibration process of the WL variables. The top-down method assists to quantify the water loss by the record and observation throughout the distribution network. In addition, the BABE approach gives a specific water leakage and burst information. The geometrical mean method is used to forecast the population up to 2023 along with their fiscal value by the uniform tariff method. With respect to the revenue lost, 42575 Br and 42664 Br or in 1562$ and 1566$ were lost in 2017 and 2018, respectively. The next five-year population was forecasted to estimate the possible amount of water to be saved, which was about 549627 m3 and revenue 65,111$ to make the system more efficient. The results suggested that the majority of losses were due to several components of the distribution system including pipe-joint failure, relatively older age pipes, poor repairing and maintenance of water taps, pipe joints and shower taps, negligence of the consumer and unreliable water supply. As per the research findings, recommendations were proposed on minimizing water leakage.

ACS Style

Suraj Kumar Bhagat; Tiyasha; Wakjira Welde; Olana Tesfaye; Tran Minh Tung; Nadhir Al-Ansari; Sinan Q. Salih; Zaher Mundher Yaseen. Evaluating Physical and Fiscal Water Leakage in Water Distribution System. Water 2019, 11, 2091 .

AMA Style

Suraj Kumar Bhagat, Tiyasha, Wakjira Welde, Olana Tesfaye, Tran Minh Tung, Nadhir Al-Ansari, Sinan Q. Salih, Zaher Mundher Yaseen. Evaluating Physical and Fiscal Water Leakage in Water Distribution System. Water. 2019; 11 (10):2091.

Chicago/Turabian Style

Suraj Kumar Bhagat; Tiyasha; Wakjira Welde; Olana Tesfaye; Tran Minh Tung; Nadhir Al-Ansari; Sinan Q. Salih; Zaher Mundher Yaseen. 2019. "Evaluating Physical and Fiscal Water Leakage in Water Distribution System." Water 11, no. 10: 2091.