Small baselines techniques of time series InSAR to monitor and predict land subsidence causing flood vulnerability in Sidoarjo, Indonesia

Hayati, Noorlaila; Widodo, Amien; Kurniawan, Akbar; Sanjiwani, I Dewa Made Amertha; Darminto, Mohammad Rohmaneo; Yudha, Imam Satria; Sumantyo, Josaphat Tetuko Sri

doi:10.1080/19475705.2022.2109518

Cited by 7 publications

(4 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To some cases, the flooding is simple due to poor drainage system such as in Sarawak, Malaysia [9] and Ogun, Nigeria [10] or unpredicted volume of rainfall such as in Attica, Greece [11] and Mar Menor, Spain [12]. Especially for the case of Banjarasri and Kedungbanteng in Sidoarjo-Indonesia, the flood is caused by the land subsidence due to mining activities [13]. Areas exhibiting concerning levels of ground deformation, indicating they are "at-risk," are located around the Lapindo region and extend towards the northern part of Sidoarjo.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Rapid Assessment for Emergency Infrastructures in Responding Flood related to Mining Activity: Case Study of Banjarasri and Kedungbanteng Flood, Sidoarjo, Indonesia

Pamungkas,

Elysiyah,

Mahardhika

et al. 2024

BIO Web Conf.

View full text Add to dashboard Cite

Flooding has become the most frequent and impactful disaster events in the Indonesian cities. The Indonesian disaster board also noted that flood events has increasing pattern for today and future time. One of the strategic ways to decrease the impact of flooding is delivering the emergency infrastructures to the needs immediately. The faster delivery makes less impact of disaster events. Therefore, this paper synthesizes a rapid model of assessment from the full assessment of emergency infrastructure need in Sidoarjo Flooding 2020. During the synthesize, the model can reduce duration of assessment from 148 days to four days only. Within the time, eight main steps include finding relevant emergency infrastructures, determining emergency infrastructures needed, assessing emergency infrastructures needed and confirming the actual emergency infrastructure needed. Those eight steps should be taken by encouraging more FGD and in-depth interviews for key relevant stakeholders, guided by specific checklist and filling the gap forms. Strict forms and guidelines provided to help the process in achieving four days assessment period. Consequently, the authorities can use the output assessment for emergency infrastructure program delivery starting from the fifth days after the events.

show abstract

Section: Introductionmentioning

confidence: 99%

“…The land subsidence in those two villages occurs 2017-2021 for about 200 mm/year. The land subsidence has caused a changing on water flow in drainage and irrigation systems causing flooding [13]. The flooding in the two villages has 30-50 cm depth with two months duration.…”

Section: Introductionmentioning

confidence: 99%

Rapid Assessment for Emergency Infrastructures in Responding Flood related to Mining Activity: Case Study of Banjarasri and Kedungbanteng Flood, Sidoarjo, Indonesia

Pamungkas,

Elysiyah,

Mahardhika

et al. 2024

BIO Web Conf.

View full text Add to dashboard Cite

show abstract

“…Similarly, Sidoarjo, characterised by a high population density and a mix of small and large industries, shares this inundation challenge. Malang (Handayani & Prawito 2022 ; Hayati et al 2022 ), renowned as an educational hub, has recently fallen prey to these regular flooding events, which were once infrequent (Hapsari et al 2023; Hariyono & Kurniawan 2022 ; Utami & Bisri 2014 ). All these cities were interconnected by the Brantas River Basin, originating in Sumber Brantas Village, Bumiaji Sub-District, Batu City.…”

Section: Introductionmentioning

confidence: 99%

Analysis of factors in community willingness to manage floods in East Java during the pandemic

Noerhayati,

Mustika,

Ingsih

et al. 2024

Jàmbá Journal of Disaster Risk Studies

View full text Add to dashboard Cite

“…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

View full text Add to dashboard Cite

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.

show abstract

Small baselines techniques of time series InSAR to monitor and predict land subsidence causing flood vulnerability in Sidoarjo, Indonesia

Cited by 7 publications

References 63 publications

Rapid Assessment for Emergency Infrastructures in Responding Flood related to Mining Activity: Case Study of Banjarasri and Kedungbanteng Flood, Sidoarjo, Indonesia

Rapid Assessment for Emergency Infrastructures in Responding Flood related to Mining Activity: Case Study of Banjarasri and Kedungbanteng Flood, Sidoarjo, Indonesia

Analysis of factors in community willingness to manage floods in East Java during the pandemic

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

Contact Info

Product

Resources

About