Introducing a new framework for mapping subsidence vulnerability indices (SVIs): ALPRIFT

Applying hesitant q-rung orthopair fuzzy sets to evaluate uncertainty in subsidence causes factors

Land subsidence is a widespread problem impacting communities worldwide. Understanding the causes and factors of land subsidence is crucial for identifying and prioritizing effective mitigation measures. One of the main reasons for prioritizing land subsidence causes is the potential impact on infrastructure and the environment. The main objective of this paper is to emphasize the importance of prioritizing the causes of land subsidence. By understanding and prioritizing the factors contributing to land subsidence based on their impact and urgency, the aim is to develop targeted strategies for mitigation, inform policy decisions, and prevent further exacerbation of this problems. The study comprises three phases, where experts in the field provide their opinions and propose a robust hybrid framework. This framework integrates the Failure Mode and Effect Analysis (FMEA) and Step-wise Weight Assessment Ratio Analysis (SWARA) with Hesitant q-rung orthopair fuzzy set (Hq-ROFS). The performance of the proposed technique was then compared with two other decision-making techniques for evaluating and ranking land subsidence causes. According to the results, extraction of groundwater, excessive irrigation using groundwater, and oxidation and drainage of organic soils were identified as primary drivers of subsidence.

40-years of Lake Urmia restoration research: Review, synthesis and next steps

Public concern over environmental issues such as ecosystem degradation is high. However, restoring coupled human-natural systems requires integration across many science, technology, engineering, management, and governance topics that are presently fragmented. Here, we synthesized 544 peer-reviewed articles published through September 2020 on the desiccation and nascent recovery of Lake Urmia in northwest Iran. We answered nine questions of scientific and popular interest about causes, impacts, stabilization, recovery, and next steps. We find: (1) Expansion of irrigated agriculture, dam construction, and mismanagement impacted the lake more than temperature increases and precipitation decreases. (2) Aerosols from Lake Urmia's exposed lakebed are negatively impacting human health. (3) Researchers disagree on how a new causeway breach will impact salinity, evaporation, and ecosystems in the lake's north and south arms. (4) Most researchers tried to restore to a single, uniform, government specified lake level of 1274.1 m intended to recover Artemia. (5) The Iranian government motivated and funded a large and growing body of lake research. (6) Ecological and limnological studies mostly focused on salinity, Artemia, and Flamingos. (7) Few studies shared data, and only three studies reported engagement with stakeholders or managers. (8) Researchers focused on an integration pathway of climate downscaling, reservoirs, agricultural water releases, and lake level. (9) Numerous suggestions to improve farmer livelihoods and governance require implementation. We see an overarching next step for lake recovery is to couple human and natural system components. Examples include: (a) describe and monitor the system food webs, hydrologic, and human components; (b) adapt management to monitored conditions such as lake level, lake evaporation, lake salinity, and migratory bird populations; (c) improve livelihoods for poor, chronically stressed farmers beyond agriculture; (d) manage for diverse ecosystem services and lake levels; (e) engage all segments of society; (f) integrate across restoration topics while building capacity to share data, models, and code; and (g) cultivate longer-term two-way exchanges and public support. These restoration steps apply in different degrees to other Iranian ecosystems and lakes worldwide.

Investigating meteorological/groundwater droughts by copula to study anthropogenic impacts

A critical understanding of the water crisis of Lake Urmia is the driver in this paper for a basin-wide investigation of its Meteorological (Met) droughts and Groundwater (GW) droughts. The challenge is to formulate a data-driven modelling strategy capable of discerning anthropogenic impacts and resilience patterns through using 21-years of monthly data records. The strategy includes: (i) transforming recorded timeseries into Met/GW indices; (ii) extracting their drought duration and severity; and (iii) deriving return periods of the maximum drought event through the copula method. The novelty of our strategy emerges from deriving return periods for Met and GW droughts and discerning anthropogenic impacts on GW droughts. The results comprise return periods for Met/GW droughts and their basin-wide spatial distributions, which are delineated into four zones. The information content of the results is statistically significant; and our interpretations hint at the basin resilience is already undermined, as evidenced by (i) subsidence problems and (ii) altering aquifers' interconnectivity with watercourses. These underpin the need for a planning system yet to emerge for mitigating impacts and rectifying their undue damages. The results discern that aquifer depletions stem from mismanagement but not from Met droughts. Already, migration from the basin area is detectable.

Land subsidence susceptibility mapping using PWRSTFAL framework and analytic hierarchy process: fuzzy method (case study: Damaneh-Daran Plain in the west of Isfahan Province, Iran)

Land subsidence susceptibility mapping is a necessary prerequisite for land subsidence management. Proper assessment of land subsidence requires determining parameters affecting subsidence to discover the spatial relationships with subsidence. For this purpose, two frameworks were prepared, according to the land site subsidence, eight parameters affecting the subsidence in the Damaneh-Daran plain. In the first framework, eight parameters including annual groundwater level drawdown, aquifer medium, land use, discharge, aquifer saturation thickness, net recharge, fault proximity and topography called PWRSTFAL based on weighting parameters and the second model based on optimization and compatibility of eight active layers on subsidence, were prepared by AHP-fuzzy method and the final map of subsidence vulnerability obtained. Radar images and the InSAR method validated the methods. The results show that both maps show a good correlation with radar data. Still, the map prepared by the AHP-fuzzy method offers the highest correlation with radar data and subsidence in the plain. It separates subsidence vulnerability in more detail on the whole surface of the plain. According to this model, most plain areas, especially the eastern part, are subject to subsidence, and management programs should control subsidence. The results ROC diagram obtained from the performance of the PWRSTFAL framework based on the AHP-fuzzy method was more promising than the ordinary PWRSTFAL framework.

Mapping Risk to Land Subsidence: Developing a Two-Level Modeling Strategy by Combining Multi-Criteria Decision-Making and Artificial Intelligence Techniques

Groundwater over-abstraction may cause land subsidence (LS), and the LS mapping suffers the subjectivity associated with expert judgment. The paper seeks to reduce the subjectivity associated with the hazard, vulnerability, and risk mapping by formulating an inclusive multiple modeling (IMM), which combines two common approaches of multi-criteria decision-making (MCDM) at Level 1 and artificial intelligence (AI) at Level 2. Fuzzy catastrophe scheme (FCS) is used as MCDM, and support vector machine (SVM) is employed as AI. The developed methodology is applied in Iran’s Tasuj plain, which has experienced groundwater depletion. The result highlights hotspots within the study area in terms of hazard, vulnerability, and risk. According to the receiver operating characteristic and the area under curve (AUC), significant signals are identified at both levels; however, IMM increases the modeling performance from Level 1 to Level 2, as a result of its multiple modeling capabilities. In addition, the AUC values indicate that LS in the study area is caused by intrinsic vulnerability rather than man-made hazards. Still, the hazard plays the triggering role in the risk realization.

An investigation into time-variant subsidence potentials using inclusive multiple modelling strategies

Groundwater over-abstraction due to the absence of an effective management plan is often one of the main reasons for land subsidence in aquifer areas. This paper investigates this environmental problem at Salmas plain, Iran, by using the ALPRIFT framework, an acronym of a set of seven general-purpose data layers, introduced recently by the authors. It is capable of mapping Subsidence Vulnerability Indices (SVI) and the paper investigates an innovation to transform it into Time-variant SVI (TSVI) mapping capabilities through a three module strategy: Module 1: maps SVI; Module 2: develops a predictive model for Groundwater Levels (GWL); Module 3: combines both modules to produces TSVI maps. Modules 1 and 2 employ Inclusive Multiple Modelling (IMM) practices, which promote learning from multiple models, as opposed to their ranking and selecting a 'superior' one. IMM is implemented through the same single modelling strategy for both Modules 1 and 2 at two levels: at Level 1, multiple models are constructed by three Fuzzy Logic (FL) models: Sugeno FL (SFL), Mamdani FL (MFL) and Larsen FL (LFL). (ii) At Level 2, FL models at Level 1 are reused by Support Vector Machine (SVM) as the combiner model. The results show that (i) the models at Level 1 are fit-for-purpose; (ii) the models at Level 2 are defensible owing to IMM strategies focussed on enhancing their accuracy and investigating their residuals; and (iii) according to TSVI maps, the north of the plain is vulnerable to hotspot areas and is exposed to subsidence risks due to unplanned over-abstraction of groundwater from the aquifer at Salmas plain.

Comparison of multi-criteria and artificial intelligence models for land-subsidence susceptibility zonation

Land subsidence (LS) in arid and semi-arid areas, such as Iran, is a significant threat to sustainable land management. The purpose of this study is to predict the LS distribution by generating land subsidence susceptibility models (LSSMs) for the Shahroud plain in Iran using three different multi-criteria decision making (MCDM) and five different artificial intelligence (AI) models. The MCDM models we used are the VlseKriterijumska Optimizacija IKompromisno Resenje (VIKOR), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Complex Proportional Assessment (COPRAS), and the AI models are the extreme gradient boosting (XGBoost), Cubist, Elasticnet, Bayesian multivariate adaptive regression spline (BMARS) and conditional random forest (Cforest) methods. We used the Receiver Operating Characteristic (ROC) curve, Area Under Curve (AUC) and different statistical indices,i.e. accuracy, sensitivity, specificity, F score, Kappa, Mean Absolute Error (MAE) and Nash-Sutcliffe Criteria (NSC)to validate and evaluate the methods. Based on the different validation techniques, the Cforest method yielded the best results with minimum and maximum values of 0.04 and 0.99, respectively. According to the Cforest model, 30.55% of the study area is extremely vulnerable to land subsidence. The results of our research will be of great help to planners and policy makers in the identification of the most vulnerable regions and the implementation of appropriate development strategies in this area.

Ground Displacement in East Azerbaijan Province, Iran, Revealed by L-band and C-band InSAR Analyses

Iran, as a semi-arid and arid country, has a water challenge in the recent decades and underground water extraction has been increased because of improper developments in the agricultural sector. Thus, detection and measurement of ground subsidence in major plains is of great importance for hazard mitigation purposes. In this study, we carried out a time series small baseline subset (SBAS) interferometric synthetic aperture radar (InSAR) analysis of 15 L-band PALSAR-2 images acquired from ascending orbits of the ALOS-2 satellite between 2015 and 2020 to investigate long-term ground displacements in East Azerbaijan Province, Iran. We found that two major parts of the study area (Tabriz and Shabestar plains) are subsiding, where the mean and maximum vertical subsidence rates are -10 and -98 mm/year, respectively. The results revealed that the visible subsidence patterns in the study area are associated with either anthropogenic activities (e.g., underground water usage) or presence of compressible soils along the Tabriz-Shabestar and Tabriz-Azarshahr railways. This implies that infrastructure such as railways and roads is vulnerable if progressive ground subsidence takes over the whole area. The SBAS results deduced from L-band PALSAR-2 data were validated with field observations and compared with C-band Sentinel-1 results for the same period. The C-band Sentinel-1 results showed good agreement with the L-band PALSAR-2 dataset, in which the mean and maximum vertical subsidence rates are -13 and -120 mm/year, respectively. For better visualization of the results, the SBAS InSAR velocity map was down-sampled and principal component analysis (PCA) was performed on ~3600 randomly selected time series of the study area, and the results are presented by two principal components (PC1 and PC2).

A generalized linear stochastic model for lake level prediction

Endorheic lakes are one of the most important factors of an environment. Regarding their morphology, these lakes, in particular saline lakes, are much more sensitive and can either benefit or pose a threat to their surroundings. Thus, constant monitoring of such lakes' water level, modeling and analyzing them for future planning and management policies is vitally important. We proposed a generalized linear stochastic model (GLSM) for forecasting the weekly and monthly Urmia lake water levels, the sixth-largest saltwater lake on Earth. In this methodology, three approaches are defined to pre-process data. The first approach is merely based on the differencing method, while the second and third are a one-step (the combination of de-trending with standardization and spectral analysis) and two-step (the combination of the 2nd approach with normalization transform) preprocessing, respectively. A thorough comparison of the GLSM results with eminence nonlinear AI models (Adaptive Neuro-Fuzzy Inference Systems, ANFIS, Multilayer Perceptron, MLP, Gene Expression Programming, GEP, Support Vector Machine with Firefly algorithm, SVM-FFA, and Artificial Neural Networks ANN) showed that by using an appropriate method that delivers accurate information of the entailing terms in time series, it is possible to model Urmia lake level with acceptable precision. Concisely, the GSLM with coefficients of determination (R²) 99.957% and root mean squared error (RMSE) of 2.121% outperformed the SVM-FFA with R² 99.59%, RMSE 3.27%, ANN with R² 99.56%, RMSE 3.3%, ANFIS with R² 98.9%, RMSE 4.3%, GP with R² 99.89%, RMSE 3.47%, GEP with R² 94.75%, RMSE 4.15% for forecasting weekly time series. In forecasting monthly time series, the GLSM method with R² 99.517% and RMSE 6.91% also outperformed GEP R² 91.95%, RMSE 15.3%, ANFIS R² 92.85%, RMSE 47.55% models. Consequently, GSLM proved that by applying proper comprehensible linear techniques promising results can be obtained rather than using sophisticated AI methods.

Earth fissure hazard prediction using machine learning models

Earth fissures are the cracks on the surface of the earth mainly formed in the arid and the semi-arid basins. The excessive withdrawal of groundwater, as well as the other underground natural resources, has been introduced as the significant causing of land subsidence and potentially, the earth fissuring. Fissuring is rapidly turning into the nations' major disasters which are responsible for significant economic, social, and environmental damages with devastating consequences. Modeling the earth fissure hazard is particularly important for identifying the vulnerable groundwater areas for the informed water management, and effectively enforce the groundwater recharge policies toward the sustainable conservation plans to preserve existing groundwater resources. Modeling the formation of earth fissures and ultimately prediction of the hazardous areas has been greatly challenged due to the complexity, and the multidisciplinary involved to predict the earth fissures. This paper aims at proposing novel machine learning models for prediction of earth fissuring hazards. The Simulated annealing feature selection (SAFS) method was applied to identify key features, and the generalized linear model (GLM), multivariate adaptive regression splines (MARS), classification and regression tree (CART), random forest (RF), and support vector machine (SVM) have been used for the first time to build the prediction models. Results indicated that all the models had good accuracy (>86%) and precision (>81%) in the prediction of the earth fissure hazard. The GLM model (as a linear model) had the lowest performance, while the RF model was the best model in the modeling process. Sensitivity analysis indicated that the hazardous class in the study area was mainly related to low elevations with characteristics of high groundwater withdrawal, drop in groundwater level, high well density, high road density, low precipitation, and Quaternary sediments distribution.