Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Jul;30(31):77830-77849.
doi: 10.1007/s11356-023-27799-0. Epub 2023 Jun 2.

Land subsidence susceptibility mapping: comparative assessment of the efficacy of the five models

Lei Zhang  1   2 , Alireza Arabameri  3 , M Santosh  4   5 , Subodh Chandra Pal  6
Affiliations

Land subsidence susceptibility mapping: comparative assessment of the efficacy of the five models

Lei Zhang et al. Environ Sci Pollut Res Int. 2023 Jul.

Abstract

Land subsidence (LS) as a major geological and hydrological hazard poses a major threat to safety and security. The various triggers of LS include intense extraction of aquifer bodies. In this study, we present an LS inventory map of the Daumeghan plain of Iran using 123 LS and 123 non-LS locations which were identified through field survey. Fourteen LS causative factors related to topography, geology, hydrology, and anthropogenic characteristics were selected based on multi-collinearity test. Based on the results, five susceptibility maps were generated employing models and input data. The LS susceptibility models were evaluated and validated using the receiver operating characteristic (ROC) curve and statistical indices. The results indicate that the LS susceptibility maps produced have good accuracy in predicting the spatial distribution of LS in the study area. The result showed that the optimization models BA and GWO were better than the other machine learning algorithm (MLA). In addition, The BA model has 96.6% area under of ROC (AUROC) followed by GWO (95.8%), BART (94.5%), BRT (93.1%), and SVR (92.7%). The LS susceptibility maps formulated in our study can serve as a useful tool for formulating mitigation strategies and for better land-use planning.

Keywords: Daumeghan plain; Gray wolf optimizer; Machine learning; Optimization model; Susceptibility.

PubMed Disclaimer

References

    1. Abdollahi S, Pourghasemi HR, Ghanbarian GA, Safaeian R (2019) Prioritization of effective factors in the occurrence of land subsidence and its susceptibility mapping using an SVM model and their different kernel functions. Bull Eng Geol Env 78(6):4017–4034
    1. Abu-Mouti FS, El-Hawary ME (2012) Overview of artificial bee colony (ABC) algorithm and its applications. In: [ International Systems Conference SysCon]: IEEE; p. 1–6
    1. Aditiya A, Takeuchi W, Aoki Y (2017) Land subsidence monitoring by InSAR time series technique derived from ALOS-2 PALSAR-2 over Surabaya City, Indonesia. IOP Conf Ser: Earth Environ Sci 98:012010. https://doi.org/10.1088/1755-1315/98/1/012010 - DOI
    1. Aghda SF, Bagheri V, Razifard M (2018) Landslide susceptibility mapping using fuzzy logic system and its influences on mainlines in lashgarak region, Tehran, Iran. Geotech Geol Eng 36(2):915–937
    1. Alipour M, Malekmohammadi B, Ghorbani Z (2022) Mapping land subsidence susceptibility due to groundwater decline using fuzzy pixel-based models. Arab J Geosci 15:1014. https://doi.org/10.1007/s12517-022-10269-1 - DOI

LinkOut - more resources