Consecutive DInSAR and well based on the law of material conservation between land surface pressure and ground water to observe land subsidence

Nishi, Katsunoshin; Kawai, Masaaki; Cahyono, Bowo Eko; Waqar, Mirza Muhammad; Nishi, Koji; Sumantyo, Josaphat Tetuko Sri

doi:10.1080/10106049.2022.2159069

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…Due to the increased concern, many recent scientific articles addressing this subject have been published in recent years. Studies include those from Australia [10], France [11], Italy [12], Poland [13], Turkey [14], USA [15], Mexico [16], Argentina [17], Japan [18], China [3,19], Thailand [20], Indonesia [21], India [22], and Iran [23,24]. A repeating message from these investigations is that due to the extensive distribution of LS and its potentially devastating consequences for the economy and environment, there is an urgent need for LS sensitivity assessments, LS sensitivity zone assessments, and identification of the leading causes of LS.…”

Section: Introductionmentioning

confidence: 99%

Land Subsidence Susceptibility Mapping Using Interferometric Synthetic Aperture Radar (InSAR) and Machine Learning Models in a Semiarid Region of Iran

Gharechaee

Samani

Sigaroodi

et al. 2023

Land

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Most published studies identify groundwater extraction as the leading cause of land subsidence (LS). However, the causes of LS are not only attributable to groundwater extraction. Other land-use practices can also affect the occurrence of LS. In this study, radar interferometric techniques and machine learning (ML) models were used for the prediction, susceptibility zoning, and prioritization of influential variables in the occurrence of LS in the Bakhtegan basin. The LS rate was characterized by applying an interferometric synthetic aperture radar (InSAR). The recursive feature elimination (RFE) method was used to detect and select the dominant combination of indicators to prepare an LS susceptibility map. Three ML models, including random forest (RF), k-nearest neighbors (KNN), and classification and regression trees (CART), were used to develop predictive models. All three models had acceptable performance. Among the ML models, the RF model performed the best (i.e., Nash–Sutcliffe efficiency, Kling–Gupta efficiency, correlation coefficient, and percent bias metrics of 0.76, 0.78, 0.88, and 0.70 for validating phase, respectively). The analysis conducted on all three ML model outputs showed that high and very high LS susceptibility classes were located on or near irrigated agricultural land. The results indicate that the leading cause of land LS in the study region is not due to groundwater withdrawals. Instead, the distance from dams and the proximity to anticlines, faults, and mines are the most important identifiers of LS susceptibility. Additionally, the highest probability of LS susceptibility was found at distances less than 18 km from synclines, 6 to 13 km from anticlines, 23 km from dams, and distances less than 20 to more than 144 km from mines. The validated methods presented in this study are reproducible, transferrable, and recommended for mapping LS susceptibility in semiarid and arid climate zones with similar environmental conditions.

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