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Siraj Muhammed Pandhiani
General Studies Department, Jubail University College, Royal Commission Jubail & Yanbu, Saudi Arabia

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Original paper
Published: 03 July 2021 in International Journal of Environmental Science and Technology
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Over the years, many organizations across the globe have conducted various studies pertaining to air pollution and its ill effects. The results of these studies substantially conclude that a plethora of people succumbs to the adversities caused by the ever-increasing air pollutants. In this investigation, M5P, random forest (RF)- and Gaussian process (GP)-based approaches are used to predict the tropospheric ozone for Amritsar, Punjab state of India, metropolitan area. The models proposed were based on ten input parameters viz. particulate matter PM2.5, particulate matter PM10, sulphur dioxide (SO2), nitrogen dioxide (NO2), nitric oxide (NO), ammonia (NH3), temperature (T), solar radiation (SR), wind direction (WD) and wind speed (WS), while the tropospheric ozone (O3) was an output parameter. Three most popular statistical parameters such as correlation coefficient (CC), mean absolute error (MAE) and root mean square error (RMSE) were used for the assessment of the developed models. In comparison, it was found that better results were achieved with random forest-based model with CC value as 0.8850, MAE value as 0.0593 and RMSE value as 0.0772 for testing stage. The suggested models are expected to save cost of instrument, cost of labour work, time and contribute to greater accuracy. A result of sensitivity investigation concludes that the solar radiation is the most influencing parameter in estimating the actual values of O3 based on the current data set.

ACS Style

P Sihag; Sm Pandhiani; V Sangwan; M Kumar; A Angelaki. Estimation of ground-level O3 using soft computing techniques: case study of Amritsar, Punjab State, India. International Journal of Environmental Science and Technology 2021, 1 -8.

AMA Style

P Sihag, Sm Pandhiani, V Sangwan, M Kumar, A Angelaki. Estimation of ground-level O3 using soft computing techniques: case study of Amritsar, Punjab State, India. International Journal of Environmental Science and Technology. 2021; ():1-8.

Chicago/Turabian Style

P Sihag; Sm Pandhiani; V Sangwan; M Kumar; A Angelaki. 2021. "Estimation of ground-level O3 using soft computing techniques: case study of Amritsar, Punjab State, India." International Journal of Environmental Science and Technology , no. : 1-8.

Research article civil engineering
Published: 15 January 2021 in Arabian Journal for Science and Engineering
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Fiber-reinforced plastic (FRP) rebars can be the futuristic potential reinforcing material in place of mild steel (MS) rebars which are highly prone to corrosion. However, the bond properties of the FRP rebars are not consistent with those of mild steel rebars. Therefore, determination of bond strength properties of FRP rebars becomes essential. In this study, an investigation was conducted on 222 samples for bond strength data set for FRP rebars using various soft computing techniques such as multilinear regression, random forests, random tree, M5P, bagged-M5P tree, stochastic-M5P, and Gaussian process. Outcomes of accuracy assessment parameters, i.e., CC, MAE, and RMSE, suggest that bagged-M5P tree-based model is outperforming than other developed models CC, MAE, and RMSE whose values are 0.9530, 0.8970, and 1.2531, respectively, for testing stages. On assessing the data and the results, it was found that GP_PUK model is more appropriate than GP_RBF-based model for predicting the bond strength of FRP (MPa). On comparison of the RF and RT models, it was concluded that RF-based model performs better than RT models with CC, MAE, and RMSE values of 0.9427, 0.8674, and 1.3424, respectively, for testing stages. The results of the study also suggest that bagged-M5P model attains higher correlation with lesser RMSE values. Taylor diagram also verifies that bagged-M5P model performs better than other developed models. Sensitivity analysis suggests that bar embedment length to bar diameter (l/d) is the most influencing parameter for the prediction of bond strength of FRP.

ACS Style

Mohindra Singh Thakur; Siraj Muhammed Pandhiani; Veena Kashyap; Ankita Upadhya; Parveen Sihag. Predicting Bond Strength of FRP Bars in Concrete Using Soft Computing Techniques. Arabian Journal for Science and Engineering 2021, 46, 4951 -4969.

AMA Style

Mohindra Singh Thakur, Siraj Muhammed Pandhiani, Veena Kashyap, Ankita Upadhya, Parveen Sihag. Predicting Bond Strength of FRP Bars in Concrete Using Soft Computing Techniques. Arabian Journal for Science and Engineering. 2021; 46 (5):4951-4969.

Chicago/Turabian Style

Mohindra Singh Thakur; Siraj Muhammed Pandhiani; Veena Kashyap; Ankita Upadhya; Parveen Sihag. 2021. "Predicting Bond Strength of FRP Bars in Concrete Using Soft Computing Techniques." Arabian Journal for Science and Engineering 46, no. 5: 4951-4969.

Journal article
Published: 01 July 2020 in Journal of Irrigation and Drainage Engineering
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A reliable and continuous streamflow simulation capability is essential for systematic management of water resource systems. Thus, predicting streamflow is important for water management and flood control. This study evaluated the effectiveness of a few data-driven procedures, such as the least squares support vector machine (LS-SVM), M5P tree, and random forest (RF) algorithm for estimating streamflows of the Bernam and Tualang rivers of Malaysia. Three standard statistical measures, i.e., correlation coefficient (CE), root mean square error (RMSE), and mean absolute error (MAE), were used to evaluate the performance of the developed model. The performance of RF-based models was found to be higher than that of LS-SVM and M5P-based models with respect to predicting streamflow for both the rivers.

ACS Style

Siraj Muhammed Pandhiani; Parveen Sihag; Ani Bin Shabri; Balraj Singh; Quoc Bao Pham. Time-Series Prediction of Streamflows of Malaysian Rivers Using Data-Driven Techniques. Journal of Irrigation and Drainage Engineering 2020, 146, 04020013 .

AMA Style

Siraj Muhammed Pandhiani, Parveen Sihag, Ani Bin Shabri, Balraj Singh, Quoc Bao Pham. Time-Series Prediction of Streamflows of Malaysian Rivers Using Data-Driven Techniques. Journal of Irrigation and Drainage Engineering. 2020; 146 (7):04020013.

Chicago/Turabian Style

Siraj Muhammed Pandhiani; Parveen Sihag; Ani Bin Shabri; Balraj Singh; Quoc Bao Pham. 2020. "Time-Series Prediction of Streamflows of Malaysian Rivers Using Data-Driven Techniques." Journal of Irrigation and Drainage Engineering 146, no. 7: 04020013.

Journal article
Published: 30 April 2020 in Remote Sensing
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The aim of the present study was to explore the correlation between the land-use/land cover change and the flash-flood potential changes in Zăbala catchment (Romania) between 1989 and 2019. In this regard, the efficiency of GIS, remote sensing and machine learning techniques in detecting spatial patterns of the relationship between the two variables was tested. The paper elaborated upon an answer to the increase in flash flooding frequency across the study area and across the earth due to the occurred land-use/land-cover changes, as well as due to the present climate change, which determined the multiplication of extreme meteorological phenomena. In order to reach the above-mentioned purpose, two land-uses/land-covers (for 1989 and 2019) were obtained using Landsat image processing and were included in a relative evolution indicator (total relative difference-synthetic dynamic land-use index), aggregated at a grid-cell level of 1 km2. The assessment of runoff potential was made with a multilayer perceptron (MLP) neural network, which was trained for 1989 and 2019 with the help of 10 flash-flood predictors, 127 flash-flood locations, and 127 non-flash-flood locations. For the year 1989, the high and very high surface runoff potential covered around 34% of the study area, while for 2019, the same values accounted for approximately 46%. The MLP models performed very well, the area under curve (AUC) values being higher than 0.837. Finally, the land-use/land-cover change indicator, as well as the relative evolution of the flash flood potential index, was included in a geographically weighted regression (GWR). The results of the GWR highlights that high values of the Pearson coefficient (r) occupied around 17.4% of the study area. Therefore, in these areas of the Zăbala river catchment, the land-use/land-cover changes were highly correlated with the changes that occurred in flash-flood potential.

ACS Style

Romulus Costache; Quoc Bao Pham; Ema Corodescu-Roșca; Cătălin Cîmpianu; Haoyuan Hong; Nguyen Thi Thuy Linh; Chow Ming Fai; Ali Najah Ahmed; Matej Vojtek; Siraj Muhammed Pandhiani; Gabriel Minea; Nicu Ciobotaru; Mihnea Cristian Popa; Daniel Constantin Diaconu; Binh Thai Pham. Using GIS, Remote Sensing, and Machine Learning to Highlight the Correlation between the Land-Use/Land-Cover Changes and Flash-Flood Potential. Remote Sensing 2020, 12, 1422 .

AMA Style

Romulus Costache, Quoc Bao Pham, Ema Corodescu-Roșca, Cătălin Cîmpianu, Haoyuan Hong, Nguyen Thi Thuy Linh, Chow Ming Fai, Ali Najah Ahmed, Matej Vojtek, Siraj Muhammed Pandhiani, Gabriel Minea, Nicu Ciobotaru, Mihnea Cristian Popa, Daniel Constantin Diaconu, Binh Thai Pham. Using GIS, Remote Sensing, and Machine Learning to Highlight the Correlation between the Land-Use/Land-Cover Changes and Flash-Flood Potential. Remote Sensing. 2020; 12 (9):1422.

Chicago/Turabian Style

Romulus Costache; Quoc Bao Pham; Ema Corodescu-Roșca; Cătălin Cîmpianu; Haoyuan Hong; Nguyen Thi Thuy Linh; Chow Ming Fai; Ali Najah Ahmed; Matej Vojtek; Siraj Muhammed Pandhiani; Gabriel Minea; Nicu Ciobotaru; Mihnea Cristian Popa; Daniel Constantin Diaconu; Binh Thai Pham. 2020. "Using GIS, Remote Sensing, and Machine Learning to Highlight the Correlation between the Land-Use/Land-Cover Changes and Flash-Flood Potential." Remote Sensing 12, no. 9: 1422.

Journal article
Published: 26 January 2017 in American Journal of Educational Research
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The attributes of effective teaching in higher education remains controversial and has never been conclusive. The purpose of this study is to determine the factors affecting the students’ perceptions of teaching effectiveness, and how the instructor and course attributes can significantly influence teaching effectiveness as measured by students in course evaluation surveys. The study analyzed 3,798 student evaluations of faculty at Jubail University College using factor analysis to find out the factors loading and average extract variance value. The study predicted that there is a significant relationship between the five dimensions of teaching and students’ ratings of teaching effectiveness (i.e. instructor’s personality, knowledge, teaching ability, marking and grading policy, and course attributes and learning outcomes). The findings support the hypothesis that there is a significant relationship between effective teaching dimensions and students ratings. The study contributes to the body of literature on evaluation of teaching effectiveness in Saudi higher education.

ACS Style

Tayfour Abdalla Mohammed; Siraj Muhammed Pandhiani. Analysis of Factors Affecting Student Evaluation of Teaching Effectiveness in Saudi Higher Education: The Case of Jubail University College. American Journal of Educational Research 2017, 5, 464 -475.

AMA Style

Tayfour Abdalla Mohammed, Siraj Muhammed Pandhiani. Analysis of Factors Affecting Student Evaluation of Teaching Effectiveness in Saudi Higher Education: The Case of Jubail University College. American Journal of Educational Research. 2017; 5 (5):464-475.

Chicago/Turabian Style

Tayfour Abdalla Mohammed; Siraj Muhammed Pandhiani. 2017. "Analysis of Factors Affecting Student Evaluation of Teaching Effectiveness in Saudi Higher Education: The Case of Jubail University College." American Journal of Educational Research 5, no. 5: 464-475.