| Hybrid decision tree-based machine learning models for short-term water quality prediction (Highly Cited Paper) H Lu, X Ma Chemosphere 249, 126169, 2020 | 540 | 2020 |
| Oil and Gas 4.0 era: A systematic review and outlook H Lu, L Guo, M Azimi, K Huang Computers in Industry 111, 68-90, 2019 | 357 | 2019 |
| Blockchain technology in the oil and gas industry: A review of applications, opportunities, challenges, and risks (Highly Cited Paper) H Lu, K Huang, M Azimi, L Guo IEEE Access 7 (1), 41426 - 41444, 2019 | 319 | 2019 |
| Leakage detection techniques for oil and gas pipelines: State-of-the-art (Highly Cited Paper) H Lu, T Iseley, S Behbahani, L Fu Tunnelling and Underground Space Technology 98, 103249, 2020 | 267 | 2020 |
| Carbon trading volume and price forecasting in China using multiple machine learning models (Highly Cited Paper) H Lu, X Ma, K Huang, M Azimi Journal of Cleaner Production 249, 119386, 2020 | 241 | 2020 |
| A hybrid algorithm for carbon dioxide emissions forecasting based on improved lion swarm optimizer (Highly Cited Paper) W Qiao, H Lu, G Zhou, M Azimi, Q Yang, W Tian Journal of Cleaner Production 244, 118612, 2020 | 208 | 2020 |
| Carbon dioxide transport via pipelines: A systematic review H Lu, X Ma, K Huang, L Fu, M Azimi Journal of Cleaner Production 266, 121994, 2020 | 176 | 2020 |
| Short-term prediction of building energy consumption employing an improved extreme gradient boosting model: A case study of an intake tower H Lu, F Cheng, X Ma, G Hu Energy 203, 117756, 2020 | 125 | 2020 |
| Short-term load forecasting of urban gas using a hybrid model based on improved fruit fly optimization algorithm and support vector machine H Lu, M Azimi, T Iseley Energy Reports 5, 666-677, 2019 | 112 | 2019 |
| Oil and gas companies' low-carbon emission transition to integrated energy companies H Lu, L Guo, Y Zhang Science of The Total Environment 686, 1202-1209, 2019 | 99 | 2019 |
| Energy price prediction using data-driven models: A decade review H Lu, X Ma, M Ma, S Zhu Computer Science Review 39, 100356, 2021 | 95 | 2021 |
| A hybrid multi-objective optimizer-based model for daily electricity demand prediction considering COVID-19 H Lu, X Ma, M Ma Energy 219, 119568, 2021 | 85 | 2021 |
| Trenchless Construction Technologies for Oil and Gas Pipelines: State-of-the-Art Review H Lu, S Behbahani, M Azimi, J Matthews, S Han, T Iseley ASCE Journal of Construction Engineering and Management 146 (6), 03120001, 2020 | 85 | 2020 |
| Ultrasonic guided wave techniques and applications in pipeline defect detection: A review X Zang, ZD Xu, H Lu, C Zhu, Z Zhang International Journal of Pressure Vessels and Piping 206, 105033, 2023 | 82 | 2023 |
| Study on leakage and ventilation scheme of gas pipeline in tunnel H Lu, K Huang, L Fu, Z Zhang, S Wu, Y Lyu, X Zhang Journal of Natural Gas Science and Engineering 53, 347-358, 2018 | 72 | 2018 |
| Lake water-level fluctuation forecasting using machine learning models: a systematic review S Zhu, H Lu, M Ptak, J Dai, Q Ji Environmental Science and Pollution Research 27, 44807–44819, 2020 | 67 | 2020 |
| US natural gas consumption prediction using an improved kernel-based nonlinear extension of the Arps decline model H Lu, X Ma, M Azimi Energy 194, 116905, 2020 | 67 | 2020 |
| Novel data-driven framework for predicting residual strength of corroded pipelines (Most Cited Paper in JPSEP) H Lu, ZD Xu, T Iseley, J Matthews ASCE Journal of Pipeline Systems Engineering and Practice 12 (4), 04021045, 2021 | 62* | 2021 |
| Prediction of offshore wind farm power using a novel two-stage model combining kernel-based nonlinear extension of the Arps decline model with a multi-objective grey wolf optimizer H Lu, X Ma, K Huang, M Azimi Renewable & Sustainable Energy Reviews 127, 109856, 2020 | 62 | 2020 |
| Machine learning approaches for estimation of compressive strength of concrete M Hadzima-Nyarko, EK Nyarko, H Lu, S Zhu European Physical Journal Plus 135, 682, 2020 | 59 | 2020 |
M. Amin AzimiVisiting Assistant ProfessorPatvirtintas el. paštas latech.edu