Land Subsidence Susceptibility Research Articles

Land Subsidence Susceptibility Mapping in Ca Mau Province, Vietnam, Using Boosting Models

The Ca Mau Peninsula, situated in the Mekong Delta of Vietnam, features low-lying terrain. In addition to the challenges posed by climate change, land subsidence in the area is exacerbated by the overexploitation of groundwater and intensive agricultural practices. In this study, we assessed the land subsidence susceptibility in the Ca Mau Peninsula utilizing three boosting machine learning models: AdaBoost, Gradient Boosting, and Extreme Gradient Boosting (XGB). Eight key factors were identified as the most influential in land subsidence within Ca Mau: land cover (LULC), groundwater depth, digital terrain model (DTM), normalized vegetation index (NDVI), geology, soil composition, distance to roads, and distance to rivers and streams. The dataset includes 2011 points referenced from the Persistent Scattering SAR Interferometry (PSI) method, of which 1011 points are subsidence points and the remaining are non-subsidence points. The sample points were split, with 70% allocated to the training set and 30% to the testing set. Following computation and execution, the three models underwent evaluation for accuracy using statistical metrics such as the receiver operating characteristic (ROC) curve, area under the curve (AUC), specificity, sensitivity, and overall accuracy (ACC). The research findings revealed that the XGB model exhibited the highest accuracy, achieving an AUC and ACC above 0.88 for both the training and test sets. Consequently, XGB was chosen to construct a land subsidence susceptibility map for the Ca Mau Peninsula. In addition, 31 subsidence points measured by leveling surveys between 2005 and 2020, provided by the Department of Survey, Mapping and Geographic Information Vietnam, were used for validating the land subsidence susceptibility from the XGB method. The findings indicate a 70.9% accuracy rate in predicting subsidence susceptibility compared to the leveling measurement points.

Land subsidence susceptibility mapping based on InSAR and a hybrid machine learning approach

Land subsidence (LS) due to natural processes or human activity can irreparably damage the environment. This study employed the quasi-permanent scatterer method to detect areas with and without subsidence in the period from 2018 to 2020. In addition, 12 factors affecting subsidence were selected to detect LS-prone areas. Information gain ratio (IGR) and frequency ratio methods were used to determine the importance and weighting of various factors and sub-factors affecting subsidence. Novel approaches, including the standard adaptive-network-based fuzzy inference system (ANFIS) algorithm and its integration with the particle swarm optimization (PSO) algorithm, yielded LS maps. The models’ predictive performance was assessed using statistical indexes such as the root mean square error (RMSE), area under the receiver operating characteristic curve (AUROC) and confusion matrix criteria (e.g., sensitivity, specificity, precision, accuracy, and recall). Finally, Shapley additive explanations approach was used to explore the importance of features in modeling. The findings showed that the subsidence pattern was V-shaped, averaging 321 mm/year. Ground-leveling and interferometric synthetic aperture radar measurements revealed a 0.93 correlation coefficient with a σ = 1.43 mm/year deformation rate. Based on IGR analysis, aquifer media, the decline of the water table, and aquifer thickness played pivotal roles in LS occurrences. In addition, the ANFIS-PSO model classified approximately 50.26 % of the study area as high and very high susceptibility. The AUROC values of ANFIS-PSO and ANFIS models for the training dataset were 0.912 and 0.807, respectively. For the testing dataset, the ANFIS-PSO model produced a higher AUROC value of 0.863, while the ANFIS model had a value of 0.771. In addition, the RMSE values for the ANFIS-PSO model were lower. Given its remarkable accuracy, the ANFIS-PSO model was deemed suitable for evaluating subsidence susceptibility in the study area.

Evaluating Machine Learning-Based Approaches in Land Subsidence Susceptibility Mapping

Land subsidence (LS) due to natural and human-driven forces (e.g., earthquakes and overexploitation of groundwater) has detrimental and irreversible impacts on the environmental, economic, and social aspects of human life. Thus, LS hazard mapping, monitoring, and prediction are important for scientists and decision-makers. This study evaluated the performance of seven machine learning approaches (MLAs), comprising six classification approaches and one regression approach, namely (1) classification and regression trees (CARTs), (2) boosted regression tree (BRT), (3) Bayesian linear regression (BLR), (4) support vector machine (SVM), (5) random forest (RF), (6) logistic regression (LogR), and (7) multiple linear regression (MLR), in generating LS susceptibility maps and predicting LS in two case studies (Semnan Plain and Kashmar Plain in Iran) with varying intrinsic characteristics and available data points. Multiple input variables (slope, aspect, groundwater drawdown, distance from the river, distance from the fault, lithology, land use, topographic wetness index (TWI), and normalized difference vegetation index (NDVI)), were used as predictors. BRT outperformed the other classification approaches in both case studies, with accuracy rates of 75% and 74% for Semnan and Kashmar plains, respectively. The MLR approach yielded a Mean Square Error (MSE) of 0.25 for Semnan plain and 0.32 for Kashmar plain. According to the BRT approach, the variables playing the most significant role in LS in Semnan Plain were groundwater drawdown (20.31%), distance from the river (17.11%), land use (14.98%), NDVI (12.75%), and lithology (11.93%). Moreover, the three most important factors in LS in Kashmar Plain were groundwater drawdown (35.31%), distance from the river (23.1%), and land use (12.98%). The results suggest that the BRT method is not significantly affected by data set size, but increasing the number of training set data points in MLR results in a decreased error rate.

Land subsidence susceptibility mapping: a new approach to improve decision stump classification (DSC) performance and combine it with four machine learning algorithms.

Land subsidence is a worldwide threat. In arid and semiarid lands, groundwater depletion is the main factor that induce the subsidence resulting in environmental damages and socio-economic issues. To foresee and prevent the impact of land subsidence, it is necessary to develop accurate maps of the magnitude and evolution of the subsidences. Land subsidence susceptibility maps (LSSMs) provide one of the effective tools to manage vulnerable areas and to reduce or prevent land subsidence. In this study, we used a new approach to improve decision stump classification (DSC) performance and combine it with machine learning algorithms (MLAs) of naïve Bayes tree (NBTree), J48 decision tree, alternating decision tree (ADTree), logistic model tree (LMT), and support vector machine (SVM) in land subsidence susceptibility mapping (LSSSM). We employ data from 94 subsidence locations, among which 70% were used to train learning hybrid models and the other 30% were used for validation. In addition, the models' performance was assessed by ROC-AUC, accuracy, sensitivity, specificity, odd ratio, root-mean-square error (RMSE), kappa, frequency ratio, and F-score techniques. A comparison of the results obtained from the different models reveals that the new DSC-ADTree hybrid algorithm has the highest accuracy (AUC = 0.983) in preparing LSSSMs as compared to other learning models such as DSC-J48 (AUC = 0.976), DSC-NBTree (AUC = 0.959), DSC-LMT (AUC = 0.948), DSC-SVM (AUC = 0.939), and DSC (AUC = 0.911). The LSSSMs generated through the novel scientific approach presented in our study provide reliable tools for managing and reducing the risk of land subsidence.

Land subsidence susceptibility mapping in urban settlements using time-series PS-InSAR and random forest model

Land subsidence in urban settlements is globally becoming prevalent and severe due to sea level rise and accelerated construction. However, few studies have analyzed the susceptibility of land subsidence in urban settlements and the subsidence rate thresholds that have a great impact on the reliability of the land subsidence susceptibility map (LSSM). This work aims to provide a novel LSSM framework for decision makers to conduct risk control in regional urban settlements. Herein, the COSMO-SkyMed Synthetic Aperture Radar (SAR) data from July 2016 to June 2021 was acquired for remote sensing interpretation in Wuhan, China. Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technology, combined with geostatistical analysis, was employed to map regional land subsidence rates. A total of 12 impact factors, identified through Pearson correlation coefficient (PCC) and multicollinearity tests, were selected as input features for the machine learning model. The performance of the model was assessed using Receiver Operating Characteristic (ROC) curves, and the segmentation of PS points for land subsidence rate was discussed. Furthermore, the correspondence between the measured ground-level observations and the predicted LSSM was compared. The results demonstrate that the random forest (RF) model outperforms other models in the test set, achieving an Area Under the Curve (AUC) of 0.940. The optimal threshold of −10 mm/year is proposed for segmenting PS points, which exhibits the strongest reliability in the distribution characteristics of the susceptibility index (SI). Combined with a high-performance hybrid model, this work is expected to provide a promising reference for land subsidence susceptibility prediction in similar urban settlements worldwide.

Enhancing a convolutional neural network model for land subsidence susceptibility mapping using hybrid meta-heuristic algorithms

Managing natural hazards such as land subsidence (LS) is important because they cause large economic and human loss. LS has become a significant challenge in South Korea due to its many abandoned coal mines. Therefore, preparing LS zoning maps is vital to controlling damage caused by LS. In this study, genetic algorithm (GA) and binary whale optimization algorithm were used to select the most influential conditioning factors from the initial set of 19 factors. Subsequently, the whale optimization algorithm, and Laplacian whale optimization algorithm (LXWOA) were utilized to determine the optimal hyperparameter values for the convolutional neural network (CNN) model. The results showed that the CNN-GA-LXWOA algorithm provided a more accurate and reliable LS susceptibility map, with an area under the receiver operating characteristic curve of 0.9606, root mean square error of 0.2974 and accuracy of 91.09%. This algorithm covered 95.6% of past subsidence occurrences in the high and very high LS susceptibility classes, demonstrating its suitability for predicting future subsidence areas. We found that six main factors (elevation, drift, lineament density, slope, distance to railroads, and railroad density) control LS occurrence in Taebaek, followed by groundwater depth, lithology, and profile curvature. The proposed model may be applied for other regions with different parameters and environmental factors due to its flexible structure. The final map created in this study provides a useful tool for better LS management to mitigate the adverse effects of this natural hazard in the study area.

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

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.

Determination of Susceptibility to the Generation of Discontinuities Related to Land Subsidence Using the Frequency Ratio Method in the City of Aguascalientes, Mexico

Land subsidence in the Aguascalientes Valley, documented since the 1980s, has developed a large number of discontinuities that damage infrastructure. There is currently no methodology to accurately predict the site and time at which a discontinuity will occur, making it difficult to make decisions in urban planning or risk management. However, it is possible to determine the susceptibility of an area to the generation of fractures based on the factors associated with their formation. This study presents a zoning method based on the ground failure susceptibility index (GFSI) in the city of Aguascalientes, using the frequency ratio (FR) method and employing the depth of the basement, the subsidence rate, the subsidence gradient, and the groundwater level drawdown as variables. The zoning method included three categories of land subsidence susceptibility to fracturing, moderate, high, and very high, which were divided using the first (3.76) and second (4.24) quartiles of the GFSI. The zoning method was created with the discontinuities reported in 2010 and was validated with data from 2022. The results obtained show that 11.19% of the discontinuities developed between 2010 and 2022 were located in a zone of moderate susceptibility, 41.97% were located in a zone of high susceptibility, and 46.87% were located in a zone classified as having very high susceptibility.

Assessing the vulnerability of Iran to subsidence hazard using a hierarchical FUCOM-GIS framework

In engineering practice, land subsidence is considered a threat to the infrastructure and lifelines massively distributed globally. In this study, a combined methodology of full consistency decision-making (FUCOM) and the geographical information system (GIS) was employed to assess the vulnerability of Iran to subsidence. Accordingly, a new hierarchical FUCOM-GIS framework was developed. The FUCOM was first used to evaluate the weight of various causative factors. Afterward, the GIS was employed to superimpose several layers to produce a land subsidence susceptibility map (LSSM) of Iran. The results revealed water stress, groundwater depletion, soil type, geological time scale, and rainfall amount as the most significant factors causing land subsidence, while the influence of several additional factors was also investigated. The LSSM was validated against interferometric synthetic aperture radar (InSAR) analyses of the Sentinel-1 for several megacities. Accordingly, Tehran, Qom, Yazd, Isfahan, Kerman, and Mashhad were recognized as the critical regions, delivering a subsidence rate between 15 and 20 cm/yr. Eventually, the assessed hazard for the power transmission lines and substations, as an exemplified lifeline, was confirmed by field reconnaissance. The results revealed the pier sinking, structural members buckling, electric insulators deviation, and cracking as the main damages to the transmission lines.

A fuzzy logic approach for land subsidence susceptibility mapping: the use of hydrogeological data

A fuzzy logic approach for land subsidence susceptibility mapping: the use of hydrogeological data

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

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.

InSAR time-series analysis and susceptibility mapping for land subsidence in Semarang, Indonesia using convolutional neural network and support vector regression

Global sea-level rise due to climate change is a critical problem for coastal cities. One of the coastal cities in Indonesia, Semarang, is in danger of being submerged by seawater due to flooding, which is exacerbated by land subsidence. Time-series analysis based on persistent scatterer (PS) interferometry usually suffers from sparse measurement points (MP), and thus does not completely cover the study area with low coherence. In this study, we utilized improved combined scatterers interferometry with optimized point scatterers (ICOPS) to increase the MP density. We also compared the optimization of the ICOPS method based on support vector regression (SVR) with convolutional neural network (CNN) algorithms. The ICOPS-CNN showed better model performance and MP density, and was less time-consuming. Further land subsidence analysis was conducted in Semarang using susceptibility mapping. The land subsidence susceptibility map from the standalone deep learning (DL) algorithm (CNN = 0.833) outperformed the standalone machine learning (ML) algorithm (SVR = 0.820). The hybrid DL algorithm with grey wolf optimizer (GWO) (CNN-GWO = 0.907) exhibited the highest accuracy, followed by the hybrid ML algorithm (SVR-GWO = 0.843). The accuracy was consistent with the comparison between the susceptibility maps and the ICOPS measurements, resulting in a susceptibility map based on CNN-GWO with the highest accuracy (0.889). Land subsidence monitoring using ICOPS based on CNN and susceptibility mapping based on a hybrid DL algorithm (CNN-GWO) can be used by local governments to improve urban development planning in Semarang and its surrounding areas.

LAND SUBSIDENCE SUSCEPTIBILITY MAPPING USING MACHINE LEARNING ALGORITHMS

Abstract. Land subsidence (LS) is one of the most challenging natural disasters that has potential consequences such as damage to infrastructures and buildings, creating sinkholes, and leading to soil destruction. To mitigate the damages caused by LS, it is necessary to determine the LS-prone areas. In this paper, LS susceptibility was assessed for Kashan Plain in Iran using Random Forest (RF) and XGBoost machine learning algorithms. For the susceptibility analysis, twelve influential factors including elevation, slope, aspect, curvature, topographic wetness index (TWI), groundwater drawdown (GWD), normalized difference vegetation index (NDVI), distance to stream (DtS), distance to road (DtR), distance to fault (DtF), lithology, and land use were taken into account. 291 LS points were used in this study which was divided into two parts of 70% and 30% for training and testing the models, respectively. The prediction power of the models and their produced LS susceptibility maps (LSSMs) were validated using the Root Mean Square Error (RMSE), R-Squared (R2), and Mean Absolute Error (MAE) values. The results showed that the XGBoost had a higher R² equal to 0.9032 compared to that of the RF which was equal to 0.8355. XGBoost model had an RMSE equal to 0.3764 cm compared to that of the RF model which was equal to 0.4906 cm. MAE for the XGBoost model was 0.1217 cm and for the RF model was 0.3050 cm. Therefore, the achieved results proved that XGBoost had better performance in this research for predicting LS values based on the measured ones.

Stacking- and voting-based ensemble deep learning models (SEDL and VEDL) and active learning (AL) for mapping land subsidence.

This contribution presents a novel methodology based on the feature selection, ensemble deep learning (EDL) models, and active learning (AL) approach for prediction of land subsidence (LS) hazard and rate, and its uncertainty in an area involving two important plains - the Minab and Shamil-Nian plains - in the Hormozgan province, southern Iran. The important features controlling LS hazard were identified by ridge regression. Then, two EDL models were constructed by stacking (SEDL) and voting (VEDL) five dense deep learning (DL) models (model 1 to model 5) for mapping LS hazard. Thereafter, the predictive model performance was assessed by a precision-recall curve and Kolmogorov-Smirnov (KS) plot. A partial dependence plot (PDP), individual conditional expectation plots (ICEP), game theory, and a sensitivity analysis were used for the interpretability of the predictive DL model. According to SEDL - a model with higher accuracy - 34% (1624 km2), 14.7% (698 km2), and 19.2% (912 km2) of the total area were classified as being of very low, low, and moderate hazards, whereas 17.7% (845 km2) and 14.4% (683 km2) of area were classified as being of high and very high hazards, respectively. Based on all interpretability techniques, aquifer loss or groundwater drawdown is the most important feature controlling LS hazard, and it having the greatest impact on the SEDL model output. Based on a Taylor diagram and R2 as model performance assessment indicators, SEDL-AL (with R2 > 95% for training and test datasets) performed better than SEDL for quantify LS rate, the rate of LS ranging between 0 and 48.1cm. The highest rate of LS occurred in the Minab plain - an area located downstream of the Minab Esteghlal dam. SEDL-AL was used to quantify the uncertainty associated with the LS rate. The observed values fell within predictions provided by SEDL-AL, which indicates a high accuracy of our predictive model. Overall, our newly developed modeling techniques are helpful tools for the spatial mapping of LS susceptibility and rate, and its uncertainty.

Application of novel hybrid model for land subsidence susceptibility mapping

Land subsidence (LS) is one of the challenging geo‐hazards in several regions of the world that is mainly caused by the overexploitation of groundwater and subsequent collapse of underground cavities. The prediction of LS is difficult due to limitations in monitoring, proper surveys, and knowledge related to the functioning and behaviour of the phenomenon. The application of remote sensing (RS) and global positioning system (GPS) for the proper location identification of LS in the ground and its implementation in susceptibility mapping can help to better constrain this geohazard. However, proper LS susceptibility maps to identify LS prone areas and to mitigate this problem in a way are generally lacking. Several methodologies have been developed for preparing LS susceptibility maps through RS techniques coupled with statistical and machine learning (ML) algorithms. Furthermore, different regions have unique natural and anthropogenic causes for the occurrence of LS, such as subsidence due to groundwater over‐exploitation, road construction, consolidation of alluvial soil, mining activities, and so forth, resulting in severe economic losses. The LS susceptibility mapping (LSSM) can help to prevent natural and human catastrophes and reduce the economic damages. ML techniques are becoming increasingly proficient at modelling such occurrences and have wide application in LSSM. This study compares the performances of single and hybrid ML models to predict and map the susceptible areas prone to LS. A combination of several algorithms helps to improve the overall results. In order to achieve this, the gradient‐boosting machine‐evolutionary genetic algorithm (GBM‐EG) was adopted in this study for LS assessment and compared to three algorithms, namely random forest, artificial neural networks, and alternating decision tree, that stand as commonly used benchmarks. This case study on LSSM was carried out in the Abozidabad watershed, Isfahan Province, Iran, and a total of 155 LS locations were identified by using GPS. Measures of 12 conditioning factors were compiled for each of the sites. These data were input into the models in five different training‐to‐validation configurations of sample ratios: D1 (90%:10%), D2 (80%:20%), D3 (70%:30%), D4 (60%:40%), and D5 (50%:50%). The performance of the algorithms were evaluated with the receiver operating characteristic curve and seed cell area index (SCAI). The results show that the best area under curve value (0.989) and SCAI value were achieved by the GBM‐EG hybrid model in an 80:20 ratio. The results demonstrate that LSSMs can be effectively produced to support planning and land use for management decisions.

Land subsidence susceptibility assessment using advanced artificial intelligence models

Land subsidence poses one of the major natural hazards around the globe that cause damage to life and property. Although several advanced models have been applied to model land subsidence susceptibility, no consensus has been reached on the most accurate models to study this phenomenon. In this work, we propose the use of the following five state-of-the-art models to calculate the susceptibility to land subsidence across a region in Iran: artificial neural network – satin bowerbird optimization (ANN-SBO), artificial neural network-water cycle algorithm (ANN-WCA), artificial neural network-chimp optimization algorithm (ANN-ChoA) and artificial neural network-crow search algorithm (ANN-CSA). We used 12 land subsidence predictors and 93 land subsidence locations as input data in the algorithms. The land subsidence locations were divided into training (65 locations or 70%) and validating (28 locations or 30%) samples. As per the importance factor analysis, the Groundwater Withdraw variable was found the most important factor among all input factors and the slope was found the least important factor among all. According to the validation procedure the most performing model, in terms of Success Rate, was WCA-ANN (AUC = 0.953), followed by ChOA-ANN (AUC = 0.944), SBO-ANN (AUC = 0.924), CSA-ANN (AUC = 0.915) and ANN (AUC = 0.913). For the Prediction Rate, the highest performance was achieved by WCA-ANN (AUC = 0.974), followed by ChOA-ANN (AUC = 0.958), SBO-ANN (AUC = 0.942), CSA-ANN (AUC = 0.931) and ANN (AUC = 0.927). The present work of such higher accuracy can be useful for the policymakers of govt. of Iran during operation work of any mega projects and implementation.

Susceptibility Analysis of Land Subsidence along the Transmission Line in the Salt Lake Area Based on Remote Sensing Interpretation

As the influence of extreme climate and human engineering activities intensifies, land subsidence frequently occurs in the Salt Lake area of Qinghai Province, China, which seriously threatens the stability of the UHV transmission line crossing the area. Current susceptibility analyses of land subsidence disasters have mostly focused on the classification of land subsidence susceptibility and have ignored the differentiation of susceptibility among different land subsidence intensities. Therefore, the land subsidence susceptibility map does not meet the operation and maintenance management needs of the UHV transmission line, let alone planning and designing of new lines in the Salt Lake area. Therefore, in this study, we proposed a susceptibility analysis of different land subsidence intensities along the transmission line in the Salt Lake area. The small baseline integrated aperture radar interferometry (SBAS-InSAR) method was used to obtain the land subsidence along the transmission line based on 67 Sentinel-1 remote sensing interpretation datasets from 2017 to 2021. Based on a combination of K-means clustering and the transmission line specifications, four annual land subsidence intensity grades were identified as 0~−2 mm/year, −2~−10 mm/year, −10~−20 mm/year, and <−20 mm/year. In addition, eight geological environmental factors were analyzed, and a multi-layer perceptron neural network (MLPNN) model was used to calculate the susceptibility of the different land subsidence intensities. The area under the curve (AUC) and practical examples were used to verify the reliability of the different land subsidence intensities susceptibility mapping. The AUC values of the four subsidence intensity grades showed that the results were accurate: the <−20 mm/year grade produced the largest AUC (0.951), with the −10~−20 mm/year, −2~−10 mm/year and 0~−2 mm/year grades producing AUCs of 0.926, 0.812, 0.879, respectively. At the same time, the susceptibility classification results of different land subsidence intensities were consistent with the interpretation and site tower deformation. The results of this study provided the distribution of land subsidence susceptibility along the transmission line, distinguished the susceptibility of different land subsidence intensities, and provided more detailed subsidence information for each transmission tower. The results provide important information for transmission line tower planning, design, protection, and operation management.

A country-wide assessment of Iran's land subsidence susceptibility using satellite-based InSAR and machine learning

Land subsidence (LS), which mainly results from poor watershed management, is a complex and nonlinear phenomenon. In the present study, LS at a country-wide assessment of Iran was mapped by using several geo-environmental conditioning factors (namely, altitude, slope degree and aspect, plan and profile curvature, distance from a river, road or fault, rainfall, geology and land use) into a machine learning algorithm-based artificial neural network (ANN), and a powerful group method of data handling (GMDH). The total dataset includes historical LS and non-LS locations, identified by the interferometric synthetic aperture radar (InSAR). The whole dataset was divided into two subsets at a ratio of 70:30 for training and validating the model, respectively. ANN- and GMDH-based LS maps were evaluated using receiver-operator characteristic (ROC) curves. The information gain ratio (IGR) was calculated to determine the relative importance of the conditioning factors. The results showed that all of the considered factors contributed significantly to the LS mapping in Iran, with geology having the strongest impact. According to the ROC curve analysis, both ANN and GMDH-based LS maps were accurate, but the map obtained by the GMDH approach had a higher accuracy than that of ANN. Southwestern, northeastern and some parts of the central region of Iran were shown to be susceptible to LS in the future. According to the GMDH susceptibility map, 10% of Iran exhibits high or very high susceptibility to LS in the future. The provinces of Hamedan and Khouzestan had the highest percentage of areas at risk of LS. According to the InSAR deformation map, 39%, 20%, 25%, 13% and 3% of the investigated areas are subject to a yearly LS of −1 to −2.5, −2.5 to −5, −5 to −7.5, −7.5 to −10 and −10 to −20 cm, respectively. The province of Razavi Khorasan in the northeast of Iran had the largest area (about 3500 km2) vulnerable to LS occurrence. Based on the LS susceptibility map, the provinces of Ardebil, Kurdistan, West and East Azerbaijan, Sistan and Baluchistan and Kermanshah, although not currently undergoing a high rate of LS, will be at high risk of severe LS in the future.

Mapping land subsidence susceptibility due to groundwater decline using fuzzy pixel-based models

Mapping land subsidence susceptibility due to groundwater decline using fuzzy pixel-based models

Land-subsidence susceptibility mapping: assessment of an adaptive neuro-fuzzy inference system–genetic algorithm hybrid model

Land-subsidence (LS) is a common geo-hazard in many regions of the world. The overexploitation of subsurface and ground water, followed by collapse of underground cavities, is one of the mechanisms that cause LS. Optimal prediction of LS is difficult due to limitations in monitoring, surveying and information about the mechanism and process of LS. Therefore, LS susceptibility mapping (LSSM) is crucial to prevent and reduce the economic damage of natural and manmade catastrophes. This study in the Isfahan Province, Iran, aimed to map LS susceptibility using a hybrid model of adaptive neuro-fuzzy inference system–genetic algorithm (ANFIS–GA) and three stand-alone machine-learning (ML) models: support vector machine, maximum entropy and ANFIS. In total, 155 LS historical points were collected, and 12 LS conditioning factors (LSCFs) were compiled for each site. These factors were elevation, slope, aspect, topographic wetness index, plan curvature, distance to stream, drainage density, groundwater withdrawal, lithology, distance to fault, normalized difference vegetation index and land use and land cover. These data were input into the models in five training-to-validation sample ratios, designated D1 (90:10), D2 (80:20), D3 (70:30), D4 (60:40) and D5 (50:50). The importance of these variables to the occurrence of LS was calculated using a random forest algorithm. Algorithm performance was evaluated through receiver operating characteristic-area under the curve analysis and the results show ANFIS–GA to be the optimal model, with average training and validation AUC values of 0.9644 and 0.9416, respectively. The results demonstrate that LSSMs can be effectively prepared to support decision makers in land use planning and allocation through the development of suitable management strategies.