Assessment through Machine Learning of Groundwater Vulnerability after Seismic Damage to Fuel Pipeline-Reference-Cited by-同舟云学术

Assessment through Machine Learning of Groundwater Vulnerability after Seismic Damage to Fuel Pipeline

Published:2024-08 Issue:3 Volume:15 Page:
ISSN:1949-1190
Container-title:Journal of Pipeline Systems Engineering and Practice
language:en
Short-container-title:J. Pipeline Syst. Eng. Pract.

Author:

Haghighi Mahdi¹^ORCID,Delnavaz Ali²,Rashvand Pooria³,Delnavaz Mohammad⁴

Affiliation:

1. Ph.D. Candidate, Dept. of Civil Engineering, Islamic Azad Univ., Qazvin Branch, Qazvin 34719-93116, Iran. ORCID: .

2. Associate Professor, Dept. of Civil Engineering, Islamic Azad Univ., Qazvin Branch, Qazvin 34719-93116, Iran (corresponding author).

3. Assistant Professor, Dept. of Civil Engineering, Islamic Azad Univ., Qazvin Branch, Qazvin 34719-93116, Iran.

4. Associate Professor, Faculty of Engineering, Dept. of Civil Engineering, Kharazmi Univ., Tehran 15719-14911, Iran.

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JPSEA2.PSENG-1543

Reference42 articles.

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2. Anderson, R. T. 2000. The natural attenuation and engineered bioremediation of benzene in petroleum-contaminated aquifers under anaerobic conditions. Boston: Univ. of Massachusetts Amherst.

3. Badv, K., and K. E. Daryani. 2010. “An investigation into the upward and lateral soil-pipeline interaction in sand using finite difference method.” Iranian J. Sci. Technol., Trans. B: Eng. 34 (B4): 433–445.

4. Assessing Groundwater Vulnerability: DRASTIC and DRASTIC-Like Methods: A Review

5. Groundwater vulnerability assessment using GIS-based DRASTIC model in Nangasai River Basin, India with special emphasis on agricultural contamination