Susceptibility Assessment Research Articles

Riverside Landslide Susceptibility Overview: Leveraging Artificial Neural Networks and Machine Learning in Accordance with the United Nations (UN) Sustainable Development Goals

Riverside landslides present a significant geohazard globally, posing threats to infrastructure and human lives. In line with the United Nations’ Sustainable Development Goals (SDGs), which aim to address global challenges, professionals in the field have developed diverse methodologies to analyze, assess, and predict the occurrence of landslides, including quantitative, qualitative, and semi-quantitative approaches. With the advent of computer programs, quantitative techniques have gained prominence, with computational intelligence and knowledge-based methods like artificial neural networks (ANNs) achieving remarkable success in landslide susceptibility assessments. This article offers a comprehensive review of the literature concerning the utilization of ANNs for landslide susceptibility assessment, focusing specifically on riverside areas, in alignment with the SDGs. Through a systematic search and analysis of various references, it has become evident that ANNs have emerged as the preferred method for these assessments, surpassing traditional approaches. The application of ANNs aligns with the SDGs, particularly Goal 11: Sustainable Cities and Communities, which emphasizes the importance of inclusive, safe, resilient, and sustainable urban environments. By effectively assessing riverside landslide susceptibility using ANNs, communities can better manage risks and enhance the resilience of cities and communities to geohazards. While the number of ANN-based studies in landslide susceptibility modeling has grown in recent years, the overarching objective remains consistent: researchers strive to develop more accurate and detailed procedures. By leveraging the power of ANNs and incorporating relevant SDGs, this survey focuses on the most commonly employed neural network methods for riverside landslide susceptibility mapping, contributing to the overall SDG agenda of promoting sustainable development, resilience, and disaster risk reduction. Through the integration of ANNs in riverside landslide susceptibility assessments, in line with the SDGs, this review aims to advance our knowledge and understanding of this field. By providing insights into the effectiveness of ANNs and their alignment with the SDGs, this research contributes to the development of improved risk management strategies, sustainable urban planning, and resilient communities in the face of riverside landslides.

Map-based soil crusting susceptibility assessment using pedotransfer Rules, CORINE and NDVI: A preliminary study in Greece

Map-based assessment of land degradation processes supports national monitoring and protection of the soil resources, in line with the Land Degradation Neutrality (LDN) and the Sustainable Development Goals (SDGs) initiatives. Soil crusting susceptibility, a complex expression of land degradation, with fundamental impact on soil functions, has not yet been estimated in Greek territory. To address this knowledge gap, current research uses well-known pedotransfer rules (PRLeB), partially adjusted to suit the local soil database format. The PRLeB, already applied at European scale, examine the soil crusting problem as a function of fundamental soil characteristics. From the first analysis a paradox emerged. Moderately and highly crusting prone soils were encountered in forested areas. This paradox indicated the need for a second formalization of the soil crusting problem (ePRLeB). The CORINE Land Cover (CLC) 2018 and a seasonal vegetation cover index (Normalized Difference Vegetation Index -NDVI) were used to divide the study area into categories and to capture the temporal variability of the problem, accordingly. Three land use categories emerged. The water bodies and the artificial areas were characterized as Non-evaluated areas (NE). Areas with considerable vegetation coverage (NDVI greater than 0.5) were characterized as Non-prone to crusting (NC), and the rest of the areas were characterized as Potentially prone to crusting (PC). The PRLeB approach was applied only to the PC areas. In both formalizations, the physicochemical and textural aspects of soil crusting adopted the same PRLeB. With the first formalization, 15% and 39% of the soils in Greece were characterized as very low and low susceptible to soil crusting respectively. With the second formalization 58 – 63% of the soils were characterized by no and 5 – 7% by very low crusting susceptibility. Current work explored two fundamentally different facets of the soil crusting problem. The first formalization, which can be expressed as the soil susceptibility to crusting, considers crusting as an inherent soil process. The second formalization, expressed as soil crusting susceptibility encounters crusting as a complex phenomenon involving natural but not anthropogenic factors. The study indicated that vegetation and seasonality are important factors in the map-based assessment of the observable soil crusting phenomenon. Also, it was highlighted that the unambiguous problem specification is a critical aspect for complex problem assessment and communication, particularly within an interdisciplinary context.

Susceptibility Analysis of Glacier Debris Flow Based on Remote Sensing Imagery and Deep Learning: A Case Study along the G318 Linzhi Section.

Glacial debris flow is a common natural disaster, and its frequency has been increasing in recent years due to the continuous retreat of glaciers caused by global warming. To reduce the damage caused by glacial debris flows to human and physical properties, glacier susceptibility assessment analysis is needed. Most research efforts consider the effect of existing glacier area and ignore the effect of glacier ablation volume change. In this paper, we consider the impact of glacier ablation volume change to investigate the susceptibility of glacial debris flow. The susceptibility to mudslide was evaluated by taking the glacial mudslide-prone ditch of G318 Linzhi section of Sichuan-Tibet Highway as the research object. First, by using a simple band ratio method with manual correction, we produced a glacial mudslide remote sensing image dataset, and second, we proposed a deep-learning-based approach using a weight-optimized glacial mudslide semantic segmentation model for accurately and automatically mapping the boundaries of complex glacial mudslide-covered remote sensing images. Then, we calculated the ablation volume by the change in glacier elevation and ablation area from 2015 to 2020. Finally, glacial debris flow susceptibility was evaluated based on the entropy weight method and Topsis method with glacial melt volume in different watersheds as the main factor. The research results of this paper show that most of the evaluation indices of the model are above 90%, indicating that the model is reasonable for glacier boundary extraction, and remote sensing images and deep learning techniques can effectively assess the glacial debris flow susceptibility and provide support for future glacial debris flow disaster prevention.

An ensemble deep-learning framework for landslide susceptibility assessment using multiple blocks: a case study of Wenchuan area, China

The improvement of the landslide susceptibility mapping (LSM) is a long-standing problem, as it provides basics for hazard mitigation. Recently, hybrid ensemble deep learning (DL) techniques have witnessed the potential for this purpose. In this paper, we proposed a novel ensemble DL model, namely GL-ResNet, which employs the conventional ResNet blocks for landslide feature extraction, long-short term memory (LSTM) structures for information storage, and a proposed GoogLeNet block (GBlk) to broaden model perception ability. To validate the model performance, a landslide inventory containing 1147 historical landslide polygons and the data of 12 landslide factors in the Wenchuan area in southwestern China, was presented and separated into training and validating dataset using a 7:3 randomly sampling ratio strategy. Based on AUC and Accuracy, GL-ResNet (0.96 and 0.909) outperformed logistic regression (0.92 and 0.851), support vector machines (0.94 and 0.884), deep belief networks (0.95 and 0.884), gated recurrent unit (0.94 and 0.884) and ResNet (0.95 and 0.894). We also explored the robustness of GL-ResNet for LSM. The results suggested that although GL-ResNet is sensitive to initial training conditions, it showed good robustness to model training method and sample ratios. In detail, GL-ResNet outperformed the conventional models in terms of fitting power and prediction performance by 0.03-0.04 and 0.02 respectively in most cases, with even greater differences in the limited training dataset.

Geological Hazard Susceptibility Analysis Based on RF, SVM, and NB Models, Using the Puge Section of the Zemu River Valley as an Example

The purpose of this study was to construct a geological hazard susceptibility evaluation and analysis model using three types of machine learning models, namely, random forest (RF), support vector machine (SVM), and naive Bayes (NB), and to evaluate the susceptibility to landslides, using the Puge section of the Zemu River valley in the Liangshan Yi Autonomous Prefecture as the study area. First, 89 shallow landslide and debris flow locations were recognized through field surveys and remote sensing interpretation. A total of eight hazard-causing factors, namely, slope, aspect, rock group, land cover, distance to road, distance to river, distance to fault, and normalized difference vegetation index (NDVI), were selected to evaluate the spatial relationship with landslide occurrence. As a result of the analysis, the results of the weighting of the hazard-causing factors indicate that the two elements of rock group and distance to river contribute most to the creation of geological hazards. After comparing all the indices of the three models, the random forest model had a higher correct area under the ROC curve (AUC) value of 0.87, root mean squared error (RMSE) of 0.118, and mean absolute error (MAE) of 0.045. The SVM model had the highest sensitivity to geological hazards. The results of geological hazard prediction susceptibility analysis matched the actual situation in the study area, and the prediction effects were good. The results of the hazard susceptibility assessment of the three models are able to provide support and help for the prevention and control of geological hazards in the same type of areas.

A hybrid of ensemble machine learning models with RFE and Boruta wrapper-based algorithms for flash flood susceptibility assessment

Flash floods are among the world most destructive natural disasters, and developing optimum hybrid Machine Learning (ML) models for flash flood susceptibility (FFS) modeling remains a challenge. This study proposed novel intelligence algorithms based on a hybrid of several ensemble ML models (i.e., Bagged Flexible Discriminant Analysis (BAFDA), Extreme Gradient Boosting (XBG), Rotation Forest (ROF) and Boosted Generalized Additive Model (BGAM)) and wrapper-based factor optimization algorithms (i.e., Recursive Feature Elimination (RFE) and Boruta) to improve the accuracy of FFS mapping at Neka-Haraz watershed in Iran. In addition, the Random Search (RS) method is proposed for meta-optimization of the developed hybrid models hyper-parameters. This study considers 20 flash flood conditioning factors (CgFs) and 380 flood and non-flood locations to create a geospatial database. The performance of each hybrid model was evaluated by area under the receiver operating characteristic (ROC) curve (AUC) and several validation methods, such as efficiency. The developed hybrid models demonstrated good performance, with BGAM-Boruta achieving the highest performance (AUC = 0.953, and Efficiency = 0.910), followed by ROF-Boruta (AUC = 0.952), ROF-RFE (AUC = 0.951), BAFDA-Boruta (AUC = 0.950), BGAM-RFE (AUC = 0.950), ROF (AUC = 0.949), BGAM (AUC = 0.948), BAFDA-RFE (AUC = 0.943), XGB-Boruta (AUC = 0.943), BAFDA (AUC = 0.939), XGB-RFE (AUC = 0.938) and XGB (AUC = 0.911). In the BGAM-Boruta model, the regional coverage was about 46% for high to very high FFS areas. Moreover, the study revealed that distance to river, slope, rainfall, altitude, and distance to road CgFs are the most significant for FFS modeling in this region.

Landslide Susceptibility Assessment for Maragheh County, Iran, Using the Logistic Regression Algorithm

Landslide susceptibility assessment is the globally approved procedure to prepare geo-hazard maps of landslide-prone areas, which are highly used in urban management and minimizing the possible disasters due to landslides. Multiple approaches to providing susceptibility maps for landslides have one specification. Logistic regression is a statistical-based model that investigates the probabilities of the events which is received extensive success in landslide susceptibility assessment. The presented study attempted to use a logistic regression application to prepare the Maragheh County hazard risk map. In this regard, several predisposing factors (e.g., elevation, slope aspect, slope angle, rainfall, land use, lithology, weathering, distance from faults, distance from the river, distance from the road, and distance from cities) are identified as main responsible for landslide occurrence and 20 historical sliding events which used to prepare hazard risk maps. As verification, the models were controlled by operating relative characteristics (ROC) curves which reported the overall accuracy for susceptibility assessment. According to the results, the region is located in a moderate to high-hazard risk zone. The north and northeast parts of Maragheh County show high suitability for landslides. Verification results of the model indicated that the AUC estimated for the training set is 0.885, and the AUC estimated for the testing set is 0.769. To justify the model, the results of the LR were comparatively checked with several benchmark learning models. Results indicated that LR model performance is reasonable.

Application of bivariate statistical techniques for landslide susceptibility mapping: A case study in Kaghan Valley, NW Pakistan

Landslides are dangerous events that threaten both human life and property. This research presents a case study of Kaghan valley catchment, which is an area of frequent landslide activity. We aim to present a comparison between the bivariate Landslide Numerical Risk Factor (LNRF), Statistical Index (SI) and Information Value (InfV) statistical models to evaluate landslide susceptibility. A total of 1556 landslides were identified using earlier reports, field surveys, and GOOGLE Earth imagery. The abundance of landslides is primarily controlled by acute deformation caused by a major thrust fault system and proximity to Hazara Kashmir Syntaxis (HKS). A landslide inventory was randomly partitioned into two datasets. 70% (1106) of landslides were used as a training phase of the models, whereas 30% (450) as validation of the three models. A spatial database of 11 conditioning factors was produced consisting of slope, aspect, elevation, lithology, land use, Topographic Wetness Index (TWI), rainfall, Stream Power Index (SPI), distance to faults, rivers and streams. All the landslide susceptibility assessment parameters were obtained from different sources and different landslide susceptibility maps were prepared on the GIS software. Performance of the three models was validated through the Receiver operator Characteristics (ROC) through success and prediction rate curves. Results show that the area under the ROC curve (AUC) for InfV, LNRF and SI models are 70.95, 83.99 and 67.56 for success rate curves and 70.75, 83.99 and 67.85 for prediction rate curves, respectively. LNRF having the highest AUC value proved to be superior for generating regional scale landslide susceptibility maps.

Modelling Water Erosion and Mass Movements (Wet) by Using GIS-Based Multi-Hazard Susceptibility Assessment Approaches: A Case Study—Kratovska Reka Catchment (North Macedonia)

Kratovska Reka is a short (17.3 km) left tributary of Kriva Reka, whose watershed (68.5 km2) is located on the northwestern slopes of the Osogovo Mountains (North Macedonia). Due to the favorable natural conditions and anthropogenic factors, the Kratovska Reka catchment is under a high risk of natural hazards, especially water erosion and landslide occurrences. For this reason, the paper presents an approach of modelling of potential erosion and areas susceptible to the above-mentioned hydro-meteorological hazards in the Kratovska River catchment. Firstly, this study analyzed the main geographical features that contribute to intensive erosion processes in the area. Then, using the Gavrilović EPM erosion potential method, an average value of 0.56 was obtained for the erosion coefficient Z, indicating areas prone to high erosion risk. Furthermore, by using landslide susceptibility analysis (LSA), terrains susceptible to landslides were identified. The results shows that 1/3 of the catchment is very susceptible to mass movements in wet conditions (landslides). According to the combined multi-hazard model, 3.13% of the total area of the Kratovska River catchment is both at high risk of landslides and under severe erosion. The Kratovska River catchment is significantly endangered by the excessive water erosion processes (39.86%), especially on the steep valley sides, i.e., terrains that are completely exposed, under sparse vegetation, and open to the effects of distribution/concentration of the rainfall amounts throughout the year. Identifying locations with the highest erosion risk serves as the initial step in defining and implementing appropriate mitigation measures across local and regional scales, thus enhancing overall resilience to environmental challenges.

Comparison of different hydrological and stability assumptions for physically-based modeling of shallow landslides

Physically-based models are reliable techniques for landslide susceptibility assessment, therefore, several models have been proposed in the literature. In this study, two different hydrological models (FSLAM and TRIGRS) and two slope stability calculation methods (infinite slope and limit equilibrium method with spherical failure surface in SCOOPS3D) were compared. The rainfall-triggered shallow landslides that occurred in Kaptanpaşa, Rize (Turkey) in 2017 were selected as a case study. The main focus is investigating the impact of landslide morphology on the success of the slope stability models. For this purpose, landslides in the inventory were grouped according to their morphological characteristics, such as the landslide depth, length, width and their ratios, and their compatibility with model assumptions (e.g. the infinite slope assumption). Then, the model performances in different landslide types were evaluated separately. To calibrate the model parameters a new time-saving, semi-automated calibration strategy, based on a sensitivity analysis, is proposed. Concretely, stability parameters c′∅′Z were calibrated via a MATLAB code by considering the area under the curve (AUC) and extreme conditions: saturated and initial conditions. Overall, SCOOPS3D is deduced to have better performance than infinite slope analysis. However, the accuracy of SCOOPS3D is revealed to decrease for landslides with a high ratio of length/depth and width/depth. From the comparison of hydrological models, FSLAM yielded more plausible results than TRIGRS with the available data. It is demonstrated that the morphological features of the landslides significantly influence the performance of the physically-based stability models. Furthermore, it is highlighted that the resolution of the Digital Elevation Model in comparison to the width and length of the landslides in an inventory can be influential in the performance of the physical model. 10 m resolution of DEM is deemed to be suitable for the study area judging the predictive performance in small landslides.

Applying a 1D Convolutional Neural Network in Flood Susceptibility Assessments—The Case of the Island of Euboea, Greece

The main scope of the study is to evaluate the prognostic accuracy of a one-dimensional convolutional neural network model (1D-CNN), in flood susceptibility assessment, in a selected test site on the island of Euboea, Greece. Logistic regression (LR), Naïve Bayes (NB), gradient boosting (GB), and a deep learning neural network (DLNN) model are the benchmark models used to compare their performance with that of a 1D-CNN model. Remote sensing (RS) techniques are used to collect the necessary flood related data, whereas thirteen flash-flood-related variables were used as predictive variables, such as elevation, slope, plan curvature, profile curvature, topographic wetness index, lithology, silt content, sand content, clay content, distance to faults, and distance to river network. The Weight of Evidence method was applied to calculate the correlation among the flood-related variables and to assign a weight value to each variable class. Regression analysis and multi-collinearity analysis were used to assess collinearity among the flood-related variables, whereas the Shapley Additive explanations method was used to rank the features by importance. The evaluation process involved estimating the predictive ability of all models via classification accuracy, sensitivity, specificity, and area under the success and predictive rate curves (AUC). The outcomes of the analysis confirmed that the 1D-CNN provided a higher accuracy (0.924), followed by LR (0.904) and DLNN (0.899). Overall, 1D-CNNs can be useful tools for analyzing flood susceptibility using remote sensing data, with high accuracy predictions.

Mitigating Landslide Hazards in Qena Governorate of Egypt: A GIS-based Neutrosophic PAPRIKA Approach


 This paper presents a novel approach to landslide susceptibility assessment in the Qena Governorate, Egypt, integrating the neutrosophic Multi-Criteria Decision-Making (MCDM) method, the Potentially All Pairwise RanKings of all possible Alternatives (PAPRIKA), and the ArcGIS weighted overlay technique. The research focuses on the quantification and prioritization of eight criteria: slope, aspect, proximity to road, soil type, proximity to river, land cover, elevation, and Lithology. These factors are evaluated under the uncertainty and indeterminacy of the neutrosophic environment by employing the PAPRIKA method. The results of the analysis are visualized and interpreted using ArcGIS weighted overlay, offering spatially explicit insights into the landslide-prone areas. This study's outcomes could significantly contribute to the overall understanding of landslide hazards in Qena, promoting better hazard management and mitigation strategies. The results of the study demonstrated varying levels of landslide susceptibility within the study area: 2% of the area was identified as having Very High Susceptibility, 17% presented High Susceptibility, 28% had Moderate Susceptibility, 44% indicated Low Susceptibility, 8% showed Very Low Susceptibility, and 1% with Practically No Susceptibility. These findings can aid local authorities and policy-makers in prioritizing areas for mitigation efforts based on their susceptibility to landslides. The study also incorporates a sensitivity analysis, exploring ten different scenarios to ensure the robustness and reliability of the results. In the first scenario, we adhere to our initial criteria weights to represent the current situation. In the second scenario, all criteria are accorded equal significance to check the model's steadfastness when no one criterion outweighs another. Scenarios three to ten each elevate the weightage of one criterion, allowing for a comprehensive understanding of each individual criterion’s influence on the decision-making process. This systematic alteration helps pinpoint the salient features driving landslides and aids in fortifying our mitigation strategies.

An Improved Flood Susceptibility Assessment in Jeddah, Saudi Arabia, Using Advanced Machine Learning Techniques

The city of Jeddah experienced a severe flood in 2020, resulting in loss of life and damage to property. In such scenarios, a flood forecasting model can play a crucial role in predicting flood events and minimizing their impact on communities. The proposed study aims to evaluate the performance of machine learning algorithms in predicting floods and non-flood regions, including Gradient Boosting, Extreme Gradient Boosting, AdaBoosting Gradient, Random Forest, and the Light Gradient Boosting Machine, using the dataset from Jeddah City, Saudi Arabia. This study identified fourteen continuous parameters and various classification variables to assess the correlation between these variables and flooding incidents in the analyzed region. The performance of the proposed algorithms was measured using classification matrices and regression matrices. The highest accuracy (86%) was achieved by the Random Forest classifier, and the lowest error rate (0.06) was found with the Gradient Boosting regressor machine. The performance of other algorithms was also exceptional compared to existing literature. The results of the study suggest that the application of these machine learning algorithms can significantly enhance flood prediction accuracy, enabling various industries and sectors to make more informed decisions.

Debris flow susceptibility assessment based on information value and machine learning coupling method: from the perspective of sustainable development.

Accurately assessing the susceptibility of debris flow disasters is of great significance for reducing the cost of disaster prevention and mitigation, as well as disaster losses. Machine learning (ML) models have been widely used in the susceptibility assessment of debris flow disasters. However, these models often have randomness in the selection of non-disaster data, which can lead to redundant information and poor applicability and accuracy of susceptibility evaluation results. To address this issue, this paper focuses on debris flow disasters in Yongji County, Jilin Province, China; optimizes the sampling method of non-disaster datasets in machine learning susceptibility assessment; and proposes a susceptibility prediction model that couples information value (IV) with artificial neural network (ANN) and logistic regression (LR) models. A debris flow disaster susceptibility distribution map with higher accuracy was drawn based on this model. The model's performance is evaluated using the area under the receiver operating characteristic curve (AUC), information gain ratio (IGR), and typical disaster point verification methods. The results show that the rainfall and topography were found to be decisive factors in the occurrence of debris flow disasters, and the IV-ANN model established in this study had the highest accuracy (AUC = 0.968). Compared to traditional machine learning models, the coupling model produced an increase in economic benefit of about 25% while reducing the average disaster prevention and control investment cost by about 8%. Based on model's susceptibility map, this paper proposes practical disaster prevention and control suggestions that promote sustainable development in the region, such as establishing monitoring systems and information platforms to aid disaster management.

Multivariate statistical algorithms for landslide susceptibility assessment in Kailash Sacred landscape, Western Himalaya

Landslide susceptibility mapping plays an imperative role in mitigating hazards and determining the future direction of developmental activities in mountainous regions. Here, we used 518 landslide occurrences and nine landslide-conditioning parameters to build landslide vulnerability models in the Kailash Sacred Landscape (KSL), India. Four multivariate statistical models were applied, namely the generalized linear model (GLM), maximum entropy (MaxEnt), Mahalanobis D2 (MD), and support vector machine (SVM), to calibrate and compare four maps of landslide susceptibility. The results demonstrated the outperformance of Mahalanobis D2 for predictability compared to other models obtained from the area under the receiver operating characteristic curve (ROC). The ensemble model data shows that 10.5% of the landscape has susceptible conditions for future landslides, whereas 89.50% of the landscape falls under the safe zone. The occurrence of landslides in the KSL is linked to the middle elevations, vicinity to water bodies, and the motorable roads. Furthermore, the observed patterns and the resulting models exhibit the major variables that cause landslides and their respective significance. The current modelling approach could provide baseline data at the regional scale to improve the developmental planning in the KSL.

Gully Erosion Susceptibility Assessment Using Different Machine Learning Algorithms: A Case Study of Shazand Watershed in Iran

Gully Erosion Susceptibility Assessment Using Different Machine Learning Algorithms: A Case Study of Shazand Watershed in Iran

Contamination of Bacillus cereus in Foods Sold at Ramadan Bazaar in Kuching, Sarawak

Every year there are reports of food poisoning involving food sold at the Ramadan bazaar and one of the causes of food poisoning is Bacillus cereus. This study aimed to determine the contamination level of B. cereus and the antimicrobial susceptibility profile of the isolates in food sold at Ramadan bazaar in Kuching, Sarawak. A total of 155 samples from five food categories were collected from six Ramadan bazaars in three locations around Kuching. Overall, 32 (21%) food samples were found positive for B. cereus. Cooked rice showed the least satisfactory levels of B. cereus among the food categories and nasi lemak was the type of food that highly contaminated with B. cereus. Antibiotic susceptibility assessment showed that B. cereus isolates were highly resistant to ampicillin, amoxicillin/clavulanic, penicillin and cefotaxime in this study. The findings of this study showed that B. cereus has the potential to cause food poisoning to Ramadan bazaar consumers due to the high mean count in some types of food. Therefore, this study can be used as basic data for B. cereus control measures in food sold at the Ramadan bazaar.

Thermokarst Lake Susceptibility Assessment Induced by Permafrost Degradation in the Qinghai–Tibet Plateau Using Machine Learning Methods

The rapidly warming climate on the Qinghai–Tibet Plateau (QTP) leads to permafrost degradation, and the thawing of ice-rich permafrost induces land subsidence to facilitate the development of thermokarst lakes. Thermokarst lakes exacerbate the instability of permafrost, which significantly alters regional geomorphology and hydrology, affecting biogeochemical cycles. However, the spatial distribution and future changes in thermokarst lakes have rarely been assessed at large scales. In this study, we combined various conditioning factors and an inventory of thermokarst lakes to assess the spatial distribution of susceptibility maps using machine-learning algorithms. The results showed that the extremely randomized trees (EXT) performed the best in the susceptibility modeling process, followed by random forest (RF) and logistic regression (LR). According to the assessment based on EXT, the high- and very high-susceptibility area of the present (2000–2016) susceptibility map was 196,222 km2, covering 19.67% of the permafrost region of the QTP. In the future (the 2070s), the area of the susceptibility map was predicted to shrink significantly under various representative concentration pathway scenarios (RCPs). The susceptibility map area would be reduced to 37.06% of the present area in RCP 8.5. This paper also performed correlation and importance analysis on the conditioning factors and thermokarst lakes, which indicated that thermokarst lakes tended to form in areas with flat topography and high soil moisture. The uncertainty of the susceptibility map was further assessed by the coefficient of variation (CV). Our results demonstrate a way to study the spatial distribution of thermokarst lakes at the QTP scale and provide a scientific basis for understanding thermokarst processes in response to climate change.

Activated alkyne-enabled turn-on click bioconjugation with cascade signal amplification for ultrafast and high-throughput antibiotic screening

Antimicrobial susceptibility testing plays a pivotal role in the discovery of new antibiotics. However, the development of simple, sensitive, and rapid assessment approaches remains challenging. Herein, we report an activated alkyne-based cascade signal amplification strategy for ultrafast and high-throughput antibiotic screening. First of all, a novel water-soluble aggregation-induced emission (AIE) luminogen is synthesized, which contains an activated alkyne group to enable fluorescence turn-on and metal-free click bioconjugation under physiological conditions. Taking advantage of the in-house established method for bacterial lysis, a number of clickable biological substances (i.e., bacterial solutes and debris) are released from the bacterial bodies, which remarkably increases the quantity of analytes. By means of the activated alkyne-mediated turn-on click bioconjugation, the system fluorescence signal is significantly amplified due to the increased labeling sites as well as the AIE effect. Such a cascade signal amplification strategy efficiently improves the detection sensitivity and thus enables ultrafast antimicrobial susceptibility assessment. By integration with a microplate reader, this approach is further applied to high-throughput antibiotic screening.

Regional landslide susceptibility assessment based on improved semi-supervised clustering and deep learning

Regional landslide susceptibility assessment based on improved semi-supervised clustering and deep learning