From the NJIT Department of Civil and Environmental Engineering, the Graduate Certificate in Environmental Engineering allows students to focus in Water Quality, Treatment and Infrastructure, Integrated Site Remediation, or Multidisciplinary Environmental Engineering. Environmental Engineers are interested in ways to protect the environment, improve water quality, and are essential in planning, designing and constructing water and wastewater treatment plants, solid waste disposal systems, site remediation approaches and emission control measures.

Environmental engineering-i

A survey on river water quality modelling using artificial intelligence models: 2000–2020

Assessment of stream quality and health risk in a subtropical Turkey river system: A combined approach using statistical analysis and water quality index

Potential toxic elements in sediment of some rivers at Giresun, Northeast Turkey: A preliminary assessment for ecotoxicological status and health risk

The Water Quality Index (WQI) is the most common indicator to characterize surface water quality. This study introduces a new ensemble machine learning model called Extra Tree Regression (ETR) for predicting monthly WQI values at the Lam Tsuen River in Hong Kong. The ETR model performance is compared with that of the classic standalone models, Support Vector Regression (SVR) and Decision Tree Regression (DTR). The monthly input water quality data including Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Electrical Conductivity (EC), Nitrate-Nitrogen ( NO 3 -N), Nitrite-Nitrogen ( NO 2 -N), Phosphate ( P O 4 3 - ), potential for Hydrogen (pH), Temperature (T) and Turbidity (TUR) are used for building the prediction models. Various input data combinations are investigated and assessed in terms of prediction performance, using numerical indices and graphical comparisons. The analysis shows that the ETR model generally produces more accurate WQI predictions for both training and testing phases. Although including all the ten input variables achieves the highest prediction performance ( R 2 t e s t = 0.98 , R M S E t e s t = 2.99 ), a combination of input parameters including only BOD, Turbidity and Phosphate concentration provides the second highest prediction accuracy ( R 2 t e s t = 0.97 , R M S E t e s t = 3.74 ). The uncertainty analysis relative to model structure and input parameters highlights a higher sensitivity of the prediction results to the former. In general, the ETR model represents an improvement on previous approaches for WQI prediction, in terms of prediction performance and reduction in the number of input parameters.

River water quality index prediction and uncertainty analysis: A comparative study of machine learning models

Hydrological droughts are characterized based on their duration, severity, and magnitude. Among the most critical factors, precipitation, evapotranspiration, and runoff  are essential in modeling t...

https://www.tandfonline.com/doi/pdf/10.1080/19942060.2020.1715844

Predicting Standardized Streamflow index for hydrological drought using machine learning models

This study explores the river-flow-induced impacts on the performance of machine learning models applied for forecasting of water quality parameters in the coastal waters in Hilo Bay, Pacific Ocean...

https://www.tandfonline.com/doi/pdf/10.1080/19942060.2018.1528480

Effect of river flow on the quality of estuarine and coastal waters using machine learning models

Silver nano particles have antimicrobial property which makes them appropriate for disinfection. Due to their antimicrobial feature, these particles are applicable for root canal irrigation...

/pdf/numerical-simulation-of-nanofluid-flow-inside-a-root-canal-6stg0j0tka.pdf

Numerical simulation of nanofluid flow inside a root canal

In this study, the boundary element method–finite element method (BEM-FEM) model is employed to investigate the sloshing and flexibility terms of elastic submerged structures on the behavior of a c...

https://tandfonline.com/doi/pdf/10.1080/19942060.2019.1619197

Shahabbodin Shamshirband

Papers

Predicting Standardized Streamflow index for hydrological drought using machine learning models

Effect of river flow on the quality of estuarine and coastal waters using machine learning models

Numerical simulation of nanofluid flow inside a root canal

Investigation of submerged structures’ flexibility on sloshing frequency using a boundary element method and finite element analysis