Land subsidence hazard assessment based on novel hybrid approach: BWM, weighted overlay index (WOI), and support vector machine (SVM)

Abstract

Land subsidence is a morphological phenomenon, which causes negative environmental and economic consequences for human societies. Therefore, identifying the areas prone to subsidence can be one of the necessary measures for reducing the potential risks. This study aims to evaluate the efficiency of the support vector machine (SVM) algorithm and weighted overlay index (WOI) models in zoning the rate of land subsidence hazard in Hashtgerd plain, Iran. First, the 19 criteria include groundwater depletion, groundwater extraction, aquifer thickness, alluvium thickness, aquifer recharge, well density, drainage density, groundwater depth, lithology, bedrock depth, average annual precipitation, average annual temperature, climate type, agricultural use, urban use, industrial use, distance from rivers and streams, distance from roads, distance from faults were considered. Then, the layers were weighed based on the Best-Worst Method (BWM). The results of BWM indicated that the factors of groundwater extraction (0.219), lithology (0.157), and groundwater depletion (0.079) have a greater effect on the potential for subsidence hazard. Moreover, the results of validation by performing ROC curve showed that the accuracy of RBF-SVM, LN-SVM, SIG-SVM, PL-SVM, and WOI were 95.7, 94.3, 94.9, 93.2, and 90%, respectively. Based on the ROC results, all of the models for preparing the subsidence hazard map in Hashtgerd plain exhibit excellent accuracy. Therefore, all of the models used here can predict the areas vulnerable to subsidence properly. In this study, the five land subsidence hazard maps were used as new input factors and integrated using fuzzy gamma-ensemble methods to make better hazard maps. The results of the ensemble model indicated that 19.3% of Hashtgerd plain is in the zone of high to very high sensitivity. The results of this study can help planners in managing and reducing the possible hazards of subsidence.

Mobilya Üretiminde Malzeme Kombinasyonu Seçimi İçin Çok Kriterli Bir Çözüm Yaklaşımı

“…Bu çalışma, mobilya endüstrisinde malzeme katman organizasyonu değerlendirme problemini karmaşık bir çok kriterli karar verme problemi olarak formüle ederek ve malzeme kombinasyonu seçimi için BWM ve WASPAS yöntemlerini bütünleştirerek yeniliğini sunmaktadır. [18], kaplama malzemesi seçimi [19], güneş paneli teknolojisi seçimi [20], rüzgar çiftliği yer seçimi [21], tedarikçi seçimi [22] ve arazi çökme tehlikesi değerlendirme [23].…”

Land subsidence risk assessment along metro lines in Shanghai using InSAR monitoring data and optical images

“…Regarding research methods, the analytic hierarchy process (AHP) stands out as the most common modeling method (Hu et al 2009;Huang et al 2012;Wang et al 2014;Lyu et al 2020;Zhang et al 2023c). Due to the development of geographic information system (GIS) and computer technology, machine learning methods such as random forest model (Xu et al 2023), boosted regression tree (Zhang et al 2023b), support vector machine (Mehrnoor et al 2023), artificial neural network (Ku and Liu 2023), and ensemble deep learning (Mohammadifar et al, 2023) are progressively being applied to risk assessment. Furthermore, methods including decision matrices (Akcin 2021;Zhao et al 2023), fuzzy logic (Mohebbi et al 2021Faryabi 2023), full consistency decision-making (Sadeghi et al 2023), evidence-weighted models (Oh and Lee 2010), and Dempster-Shafer (D-S) evidence theory (Chen et al 2014) have been effectively employed.…”

“…Furthermore, methods including decision matrices (Akcin 2021;Zhao et al 2023), fuzzy logic (Mohebbi et al 2021Faryabi 2023), full consistency decision-making (Sadeghi et al 2023), evidence-weighted models (Oh and Lee 2010), and Dempster-Shafer (D-S) evidence theory (Chen et al 2014) have been effectively employed. Secondly, in terms of research subjects, land subsidence risk assessment has primarily focused on regional scales (Zhang et al 2023c;Lyu et al 2020;Mehrnoor et al 2023). However, there is a growing consensus on the necessity and importance of conducting risk assessment focused specifically on urban infrastructure, as our understanding of the hazards of urban land subsidence deepens.…”

“…Regarding settlement data, it typically comprises cumulative settlement (Zhang et al 2023c;Nduji et al 2023) and settlement rates (Hu et al 2009;Lyu et al 2020); geological information encompasses aquifer thickness (Huang et al 2012;Faryabi 2023), deposition layer thickness (Mehrnoor et al 2023;Pacheco-Martínez et al 2015), water level (Rahmati et al 2019;Zhang et al 2023b), and surface elevation (Kim et al 2006;Faryabi 2023); socio-economic data include population density (Hu et al 2009;Bhattarai and Kondoh 2017), gross domestic product (GDP) (Zhang et al 2023c;Wang et al 2014), land use type (Hu et al 2009;Ku and Liu 2023;Mehrnoor et al 2023), and infrastructure density (Lyu et al 2020;Wang et al 2014). Moreover, factors such as climate (Mehrnoor et al 2023), precipitation (Xu et al 2023;Sadeghi et al 2023), distances to rivers, roads, and faults (Rahmati et al 2019;Mohammadifar et al 2023;Faryabi 2023), and investments in disaster prevention and reduction have been integrated into the index system due to their relevance to land subsidence. In the application of these factors, it is essential to screen and select appropriate evaluation indices based on the specific research object and foundation (Bhattarai and Kondoh 2017;Mohebbi et al 2021;Zhao et al 2023;Nduji et al 2023).…”

Integrated analysis of Hashtgerd plain deformation, using Sentinel-1 SAR, geological and hydrological data

“…In a study conducted by Mehrnoor et al, (2022) to investigate the risk of subsidence in the Hashtgerd plain, support vector machine (SVM) and weighted overlay index (WOI) models were used to classify subsidence risk zones based on 19 geological, hydrological, hydrogeological, and environmental criteria weighted using the best-to-worst method (BWM). The results of BWM indicate that groundwater abstraction, lithology, and groundwater depletion are the most influential factors in this area's subsidence 26 . Taesiri et al, (2020) created a map by combining indices of related active tectonics (IRAT) and weighted, combined morphometric indices, demonstrating a high level of tectonic activity at the center of the Hashtgerd plain.…”