Dr Adil Masood is working as an Assistant Professor at the TERI School of Advanced Studies (TERI SAS) in New Delhi, India. He earned his PhD in Civil Engineering from Jamia Millia Islamia University, India, and possesses substantial experience in both teaching and research. His doctoral work primarily focused on applied machine learning, specifically in predicting PM2.5 levels for Delhi using machine learning and deep learning techniques. Dr Adil's research revolves around the application of machine learning, deep learning and other classical approaches to address global environmental challenges. His focus areas include urban air quality, extreme weather phenomena (such as droughts, flash floods, and heat waves), severe pollution events (including wildfires, dust storms, and winter smog), precipitation patterns, and the overarching issue of climate change.
Prior to joining TERI SAS, Dr. Adil accrued professional experience as an Engineer at WAPCOS (Ministry of Jal Shakti). During this tenure, he was posted at Afghanistan, contributing to the Salma dam (Afghan India Friendship) project. Additionally, he was actively engaged in academia, serving as a Guest faculty at Jamia Millia Islamia University, Delhi, and as an Assistant Professor at Al Falah University, Faridabad. In these capacities, he shared his knowledge through lectures and conducted laboratory sessions for students at both the undergraduate and postgraduate levels.
With over 20 publications in renowned national and international journals, Dr. Adil's research has garnered widespread recognition. He actively reviews for leading journals and presents his work on diverse global platforms. Notably, one of his research works based on construction waste management, earned him the prestigious ICONSWM Excellence Award from United Nations Centre for Regional Development (UNCRD) and International Society of waste management, Air and Water (ISWMAW), highlighting his dedication to impactful research for a better tomorrow.
Publications:
• Hameed, M. M., Masood, A., Srivastava, A., Abd Rahman, N., Razalid, S. F. M., Salem, A., & Elbeltagi, A. (2024). Investigating a Hybrid Extreme Learning Machine Coupled with Dingo Optimization Algorithm for Liquefaction Triggering in Sand-Silt Mixtures.
• Ismael, B. H., Khaleel, F., Ibrahim, S. S., Khaleel, S. R., AlOmar, M. K., Masood, A., ... & Alsarayreh, A. A. (2023). Permeation Flux Prediction of Vacuum Membrane Distillation Using Hybrid Machine Learning Techniques. Membranes, 13(12), 900.
• Masood, A., Hameed, M. M., Srivastava, A., Pham, Q. B., Ahmad, K., Razali, S. F. M., & Baowidan, S. A. (2023). Improving PM2. 5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm. Scientific Reports, 13(1), 21057
• Masood, A., Niazkar, M., Zakwan, M., & Piraei, R. (2023). A Machine Learning-Based Framework for Water Quality Index Estimation in the Southern Bug River. Water, 15(20), 3543.
• Adnan, R. M., Mostafa, R. R., Dai, H. L., Heddam, S., Masood, A., & Kisi, O. (2023). Enhancing accuracy of extreme learning machine in predicting river flow using improved reptile search algorithm. Stochastic Environmental Research and Risk Assessment, 1-21.
• Masood, A., & Ahmad, K. (2023). Data-driven predictive modeling of PM2. 5 concentrations using machine learning and deep learning techniques: a case study of Delhi, India. Environmental Monitoring and Assessment, 195(1), 60.
• Alomar, M. K., Khaleel, F., Aljumaily, M. M., Masood, A., Razali, S. F. M., AlSaadi, M. A., ... & Hameed, M. M. (2022). Data-driven models for atmospheric air temperature forecasting at a continental climate region. Plos one, 17(11), e0277079.
• Masood, A., & Ahmad, K. (2023). Prediction of PM2. 5 concentrations using soft computing techniques for the megacity Delhi, India. Stochastic Environmental Research and Risk Assessment, 37(2), 625-638.
• Masood, A., Aslam, M., Pham, Q. B., Khan, W., & Masood, S. (2022). Integrating water quality index, GIS and multivariate statistical techniques towards a better understanding of drinking water quality. Environmental Science and Pollution Research, 1-17.
• Masood, A., & Ahmad, K. (2021). A review on emerging artificial intelligence (AI) techniques for air pollution forecasting: Fundamentals, application and performance. Journal of Cleaner Production, 322, 129072.
