Showing papers in "Geomatics, Natural Hazards and Risk in 2023"

Carbon storage simulation and analysis in Beijing-Tianjin-Hebei region based on CA-plus model under dual-carbon background

Abstract: Previous studies on carbon storage simulation had ignored the difference of carbon intensity among various vegetation types inner the same land use. In this paper, The PLUS model was used to predict the land use change under multi-scenarios from 2030 to 2060, and the vegetation type data were supplemented by CA model to obtain the land cover-vegetation datasets from 2030-2060. Combined with the carbon density table of vegetation type, the future land use carbon storage during 2030-2060 under multi-scenarios in Beijing-Tianjin-Hebei region were analyzed. The main conclusions were as follows: (1) The spatial distribution of carbon storage in Beijing-Tianjin-Hebei region showed a pattern of ‘high in northeast-southwest and low in southeast-northwest’; (2) The carbon storage in Beijing-Tianjin-Hebei region during 1990-2020 showed a decreasing trend; (3) During 2030-2060, the carbon storage in Beijing-Tianjin-Hebei region showed a continuous decreasing trend in the absence of policy intervention, while that under the ecological protection and farmland protection scenarios showed an increasing trend; (4) Under different development scenarios, there were obvious significances of carbon storage in spatial distribution.

Seismic hazard analysis of Silchar city located in North East India

Abstract: North East India is a highly seismically active zone of India due to the collision tectonics between the Indian and the Eurasian plate in the north and subduction tectonics along the Indo-Myanmar range (IMR) in the east. It is utmost important to investigate the seismicity of this region along with uniform hazard spectra (UHS). In this paper, seismic hazard study has been performed at the Silchar district headquarters of Assam state, located in North East India. The earthquake data have been collected from various agencies from 1762 to 2020 and the fault data has been collected from seismotectonic atlas (SEISAT). Earthquake and fault data are superimposed on the Northeast India map using MapInfo software. The fault zone is defined based upon the earthquake’s density near the fault. Seismicity parameters and maximum magnitude have been estimated using Gutenberg-Richter (G-R) relationship. The best-fitted ground motion equation is considered. The outcome is represented in terms of controlling source, seismic hazard curve and UHS for Silchar city with varying ground motions. Finally, the results are presented as PGA based upon DSHA and PSHA methods. The effect of Importance Factor (I) and Response reduction factor (R) have been investigated by comparing UHS with IS: 1893-2016 code response spectra.

Integrated use of Sentinel-1 and Sentinel-2 data and open-source machine learning algorithms for burnt and unburnt scars

Abstract: This research compares the use of the SAR (Sentinel-1) and Optical (Sentinel-2) sensors in identifying and mapping burnt and unburnt scars are rising during a bushfire in southeastern Australia and Margalla Hills, Islamabad, Pakistan, in 2019 and 2020. In order to evaluate the backscatter strength along with the Polarimetric decomposition portion, the C-band dual-polarized Sentinel-1 data was investigated to determine the magnitude of the burnt areas of forest cover in the study area. We could derive texture measurements from locally-based statistics using the Grey Level Co-occurrence Matrix (GLCM) and the backscatter coefficient. This was because of how well it picked up on differences in texture between burned and unburned scars. In contrast, Sentinel-2 optical remote sensing was employed to evaluate the extent of the burnt intensity levels for both regions utilizing the differential Normalized Burnt Ratio (dNBR). A Support Vector Machine (SVM) and Markov Random Field (MRF) classifier were utilized to investigate the study’s context. The ideal smoothing parameter is the result of incorporating the image’s spectral characteristics and spatial meaning. Sentinel-2 images were used as a foundation for both the test and training datasets, which were built from images of both unburned and burned areas broken down pixel by pixel. In both types, including spectral sensitivity and sensitivity of Polarimetric for the two groups identified after classification, the experimental findings showed a clear association between them. The algorithm’s efficiency was evaluated using the kappa coefficient and F-score calculation. Except for Sentinel-1 data in Pakistan, all fire areas have more than 0.80 accuracies. The highest precision of both Sentinel-1 and Sentinel-2 was also provided by the performance of users’ and producers’ accuracy. The entropy alpha decomposition helped define the target given by the H-a plane based on its physical properties. After the burn, the entropy and alpha values diminished and formed a pattern. However, the findings in this field validate the effectiveness of SAR sensors data and optical satellite in forest applications. The related sensitivity is highly dependent on the composition of the landscape, the geographical nature of the study area, and the severity of the burn.

Rapid mapping of seismic intensity assessment using ground motion data calculated from early aftershocks selected by GIS spatial analysis

Abstract: Following a major earthquake, disaster information services must deliver accurate damage assessment results during the emergency ‘black box’ phase when data is scarce. Seismic intensity maps contain crucial information for determining the damage in the affected area. For earthquakes with Mw between 5.5 and 7, this study proposes using GIS analysis to mine aftershock events in early aftershock sequences that are closely related to the mainshock fault, and then using these events to generate seismic intensity assessment maps. Regression curves were first obtained using a nonparametric method (rLowess) to analyse the geographical coordinates of early aftershocks. Then, a buffer of 1 or 1.5 km radius was made for the curve, and the aftershocks in the buffer were used to calculate the predicted peak ground velocity (PGV) values over a specific km-grid range. Finally, rapid mapping of seismic intensity was assessed based on the intensity scale. This straightforward and repeatable method employs seismic station data obtained shortly after the mainshock. The assessed seismic intensity accurately reflects the location and extent of the hardest hit areas and can be cross-referenced with geophysical results to accurately assess the damage in the affected areas.

Spatial interpolation of water quality index based on Ordinary kriging and Universal kriging

Abstract: Water is a very vital needed substance in order to maintain the important activities of humans. However, contaminated water can transmit diseases such as typhoid, dysentery, diarrhea, cholera and polio. Pakistan is a highly affected country due to the scarcity of safe and healthy water sources. The current study mainly focuses to determine water quality in the selected area. For this purpose, an integrated surface water quality index (SWQI) based on 18 hydrochemical parameters is employed. SWQI substantially correlates with pH, EC, SO4, HCO3 and heavy metals. Therefore, these parameters are utilized in the calculation of SWQI. The results of SWQI show that about 69.23% of samples are ‘very good quality’. Moreover, 30.77% of the total samples are of poor quality and are identified as unsuitable for drinking. Further, Ordinary kriging (OK) and Universal kriging (UK) are used to predict the SWQI at unobserved locations and map the SWQI in the study area to explore the spatial distribution. The prediction performance of the two kriging methods is assessed through cross-validation in terms of root mean square prediction error (RMSPE). It is concluded that the prediction performance of the UK is better than OK in terms of RMSPE.

Sediment yield estimation in GIS environment using RUSLE and SDR model in Southern Ethiopia

Abstract: Soil erosion and sediment yields are the current limitations and future threats to agriculture, water resources and hydropower projects particularly in developing countries. Estimating the extent and comprehending the spatial distribution of hotspot area is crucial to implement evidence-based soil and water conservation (SWC) measures with limited resources. The study used RUSLE and SDR models in ArcGIS 10.8 environment. The RUSLE model was found to be highly sensitive to C factor followed by LS factor. The result indicated that the annual soil loss varies from 0 to 359.99 t ha−1 yr−1 with 22.31 t ha−1 yr−1 as a mean annual. Besides, the estimated sediment yield ranged from 0 to 42.5 t ha−1 yr−1 with a mean value of 12.02 t ha−1 yr−1. The finding revealed that the central west (SW_5) and northeast (SW_4) parts of the watershed yield higher sediment. The result also signified that about 52.9% of the eroded materials including soil and nutrients are transferred to the outlet. The outcome of our finding undoubtedly aids in the identification of hotspot areas for the adoption of appropriate SWC measures. Hence, adopting RUSLE and SDR for Gununo watershed and another watershed having similar biophysical and environmental factors is suggested.

Multimodal multiscale characterization of cascading hazard on mountain terrain

Abstract: Extreme precipitation (rain or/and snow) in upstream areas of the Himalayan region often triggers water and sediment hazards, including flash floods, landslides, debris flow, and river damming. These hazards often interact with the environmental and human systems, resulting in acute and chronic disasters that deleteriously impact the economy, infrastructure, and livelihoods. As cascading and compound hazards become more common in the Himalayan region, a more holistic approach is needed to understand the complex interaction among human, natural, and engineered systems and tackle problems connected to several system constraints. Recent catastrophes like the 2021 Melamchi disaster cannot be attributed to a single cause; rather, they result from several geomorphologic and hydroclimatic factors and physical processes. Individual and interacting dynamics and their cascading and compounding effects occurred in various settings, from high elevations to river valleys along the Melamchi River. This study diagnoses the likely causal effects of hydroclimatic processes of the 2021 Melamchi disaster by integrating space-borne remotely sensed data, hydrologic and hydrodynamic modeling, and machine learning techniques. We have evaluated several scenarios of landslide damming and breaching and analyzed flooding impacts on major settlements across the Melamchi River. Model outputs evaluated using satellite flood imagery, field-based surveys, and published reports were used to understand the characteristics of the 2021 Melamchi disaster. We find that earlier rain episodes had saturated the soil and increased the river’s water level. Therefore, after the outburst of the landslide dam coupled with heavy downpours, a sudden release of huge water had enormous energy to inundate vast areas of flood plains and damaged settlements near riverbanks far downstream up to the Melamchi Bazar. It was not just one extreme event that led to the devastation but a series of non-extreme events. This further reinforces the idea that not just extreme events, but several not-extreme series of events may lead to disaster. Thus, the chances of such a sequence of cascading events must be investigated for risk computation along with extreme events.

Stability analysis of artificial dam in coal mine underground water reservoir based on the hydro-mechanical damage model

Abstract: A numerical hydro-mechanical damage model is proposed in this paper to investigate the stability and failure characteristics of artificial dam in coal mine underground water reservoir. The localized damage model for the coupled pore pressure and rock failure of a stressed rock is developed, and a local damage constitutive law is employed to describe its stress-strain relationship and fracture propagation. The Mohr-Coulomb criterion and maximum tension criterion are used to Judge the damage and failure of rock. Experimental data for rock samples are used to validate the proposed numerical model, and it accurately replicates the stress-strain curves and failure pattern compared with the experimental results. We then explore the influence of water pressures, cutting depth and dam strengths of artificial dam in coal mine underground water reservoir, and the simulation results show that the dam strength is the main factor affecting the strength of artificial dam. The modeling described herein is expected to assist in the management and optimization of underground water reservoirs.

Comparative analysis of GIS and RS based models for delineation of groundwater potential zone mapping

Abstract: Groundwater is a crucial natural resource that varies in quality and quantity across Khyber Pakhtunkhwa (KPK), Pakistan. Increased population and urbanization place enormous demands on groundwater supplies, reducing both their quality and quantity. This research aimed to delineate the groundwater potential zone in the Kohat region, Pakistan by integrating twelve thematic layers. In the current research, Groundwater Potential Zone (GWPZ) were created by implementing Weight of Evidence (WOE), Frequency Ratio (FR), and Information Value (IV) models of the Kohat region. In this study, we used Sentinel-2 satellite data were utilized to generate an inventory map of groundwater using machine learning algorithms in Google Earth Engine (GEE). Furthermore, the validation was done with a field survey and ground data. The inventory data was divided into training (80%) and testing (20%) datasets. The WOE, FR, and IV models are applied to assess the relationship between inventory data and groundwater factors to generate the GWPZ of the Kohat region. Finally, the current research results of Area Under Curve (AUC) technique for WOE, FR, and IV models were 88%, 91%, and 89%. The final GWPZ can aid in better future planning for groundwater exploration, management, and supply of water in the Kohat region.

Multi-objective optimization approach of shelter location with maximum equity: an empirical study in Xin Jiekou district of Nanjing, China

Abstract: Based on the inadequacies and neglect of the equity of refuge resources, refuge demands and the evacuation allocation of traditional methodologies, this study put forwards the multi-objectives layout optimization model of shelters which firstly realizes the maximum equity of shelter location. Our approach has the objectives of maximizing equity, minimizing overall egress time and minimizing the quantity of new shelters. The high-precision population is established through mobile signaling data, while the optimization model adopts a circular circulatory allocation rule derived from a gravity model. The shorter the evacuation time, the larger the shelter capacity and thus more refugees are allocated to the shelter. The evacuation time is determined by the application programming interface (API) of the Baidu Map open platform with Python, which exhibits the authentic evacuation paths and real-time traffic conditions. This study designs a three-stage algorithm ‘genetic algorithm-exhaustive method-evaluation’. The first process of algorithm calculates the minimum quantity of new shelters; the second process selects the feasible layout schemes and determines Pareto optimum solutions; and the third stage evaluates the Pareto optimum solution based on the shelter construction cost and the accessibility from shelters to emergency supply storage points to determine the best location scheme. This study regards Xin Jiekou district in Nanjing as a case area to demonstrate reliability and availability of the proposed methodology.

Spatial prediction of the geological hazard vulnerability of mountain road network using machine learning algorithms

Abstract: The current assessment index of the geological hazard vulnerability assessment for mountain road network is relatively simple, and the assessment methods used are subjective, complex, and inefficient. This study proposes a prediction model for geological hazard vulnerability assessment of mountain road network incorporating machine learning algorithms. First, based on the quantification of the characteristics of the mountain road network and the local rescue forces, an objective and reasonable index-based system of vulnerability assessment of the mountain road network was constructed by combining the population, economic, and material factors. Second, the FAHP and AHP-TOPSIS were applied for the development of the vulnerability assessment models to carry out the preliminary vulnerability assessment for different road types. Third, the results of the preliminary vulnerability assessment were used as the sample set to build a road vulnerability prediction model using SVM, RF, and BPNN algorithms. Finally, the five-fold cross-validation and statistical parameter accuracy analysis were conducted to determine the most reasonable model with the highest prediction accuracy for geological hazard vulnerability mapping of the mountain road network. The results indicated that the vulnerability prediction model based on the FAHP sample set using the RF algorithm demonstrated the highest accuracy and robustness.

Lightning fatalities and its correlation with social variables in Northwestern Mexico

Abstract: This research analyzes lightning activity in Northwestern Mexico from January 1, 2015, to December 31, 2019, and seeks to correlate it with cloud-to-ground (CG) stroke attributed fatalities and social variables. The main purpose of this paper is to define the most relevant social variables in CG stroke fatalities in order to improve our current understanding of lightning hazard. The study’s contributors anticipate that these findings will provide insights to help mitigate the loss of human life in this region. The methodology employed in this study focuses on a geospatial analysis of the CG stroke density per square kilometer related to the 50 deaths that occurred in Northwestern Mexico within the five-year time frame studied. In addition, a social-vulnerability indicator is defined by data provided by governmental agencies to assess risk based on socio-economic conditions, including level of education, access to healthcare services, access to basic human services, housing quality and space, household assets, and poverty level of the population. The social-vulnerability indicator combines available data of the general population in comparison to the occurrence of CG stroke fatalities and can contribute to an improved risk assessment of the CG stroke hazard. This geospatial analysis has found that CG stroke fatalities do not necessarily coincide with higher CG stroke density; however, other variables correlate to CG stroke fatalities statistics, including social vulnerability, population type, seasonality (time of the year), grouped age and occupation. This conclusion was determined by applying a principal component analysis (PCA) technique to the definition of the variables most closely related to CG stroke fatalities for the studied time period and region. In addition, physiographic elements were considered to explore their possible influence on the highest CG stroke density and the occurrence of CG stroke fatalities.

Spatio-temporal variations in the ecological vulnerability of the Upper Mzingwane sub-catchment of Zimbabwe

Abstract: Profiling eco-environmentally vulnerable (EV) areas can contribute to the development of mechanisms for environmental protection and sustainable management of ecological resources. Land cover change, population density, annual precipitation, mean temperature, remotely sensed indices, actual evapotranspiration, land surface temperature (LST), and runoff for the catchment for 1990, 2000, 2011, and 2020 were used to assess spatiotemporal ecological vulnerability. Spatial principal component analysis (SPCA) was used to assess thematic vulnerability for each year. The analytical hierarchical process (AHP) was then used to determine the weighting of the thematic maps to produce the final ecological vulnerability maps. Our results showed that most of the sub-basin had low and moderate vulnerability in 1990, 2000, 2011, and 2020, with the combined proportion of these areas being 80.6%, 55.45%, 83.92%, and 85.1%, respectively. The areas classified as high vulnerability decreased steadily over the investigated years, except in 2000, where an increase was observed. Most areas in the southern parts of the sub-basin were classified as moderately vulnerable, while high vulnerability values were recorded in the northern areas. The southern parts of the Upper Mzingwane basin are drier and less populated than the northern parts. The spatial architecture of vulnerability presented will help inform decision makers in mitigation planning and overall disaster risk management initiatives in the basin.

Enhancing flood risk assessment through integration of ensemble learning approaches and physical-based hydrological modeling

Abstract: This study aims to examine three machine learning (ML) techniques, namely random forest (RF), LightGBM, and CatBoost for flooding susceptibility maps (FSMs) in the Vietnamese Vu Gia-Thu Bon (VGTB). The results of ML are compared with those of the rainfall-runoff model, and different training dataset sizes are utilized in the performance assessment. Ten independent factors are assessed. An inventory map with approximately 850 flooding sites is based on several post-flood surveys. The inventory dataset is randomly split between training (70%) and testing (30%). The AUC-ROC results are 97.9%, 99.5%, and 99.5% for CatBoost, LightGBM, and RF, respectively. The FSMs developed by the ML methods show good agreement in terms of an extension with flood inundation maps developed using the rainfall-runoff model. The models’ FSMs showed 10–13% of the total area to be highly susceptible to flooding, consistent with RRI's flood map. The FSMs show that downstream areas (both urbanized and agricultural) are under high and very high levels of susceptibility. Additionally, different sizes of the input datasets are tested to determine the least number of data points having acceptable reliability. The results demonstrate that the ML methods can realistically predict FSMs, regardless of the number of training samples.

An identification method of potential landslide zones using InSAR data and landslide susceptibility

Abstract: Landslides are destructive to property, infrastructure and people in potential landslide zones. Identifying potential landslides is an important step in landslide preparedness and will help develop sustainable landslide risk management. Interferometric synthetic aperture radar (InSAR) and landslide susceptibility assessment (LSA) have poor reliability in individually identifying potential landslide zones. This study proposes a threshold model for identifying potential landslide zones that fuses the InSAR two-dimensional (vertical direction and east-west direction) deformation rates and LSA results. The deformation rate threshold for this threshold model is |DU| or |DE|>10 mm/year (DU and DE are the vertical and the east-west deformation rates, respectively), with threshold levels of LSA set to high and very high susceptibility. The criterion of potential landslide zones is ((|DU| or |DE|>10 mm/year) ∩ (high or very high susceptibility of LSA)), and points with similar deformation and susceptibility are clustered by the K-means algorithm, and the potential landslide zones are obtained by elimination, smooth and speckle removal operations. The results showed that the InSAR two-dimensional deformation rates DU and DE were −32.71 − 12.72 mm/year and −14.88 − 24.81 mm/year, respectively, during 2015–2020 in Lanzhou city. The LSA showed that very low, low, medium, high, and very high susceptibility accounted for 55.36%, 10.54%, 21.37%, 9.63%, 3.1% of the total area, respectively. Using the proposed threshold model, 117 potential landslide zones were identified in Lanzhou. The overlap rate between potential landslide zones and the landslide inventory was 40.17%, indicating that about 40% of the potential landslide zones overlapped with the landslide inventory and that about 60% were new potential landslide zones in Lanzhou. The feasibility of the threshold model in identifying potential landslides was confirmed by field research and time-series InSAR analysis on typical areas (L1, L2, L3, and L4), which had large deformation variables and landslide features. The proposed method can quickly determine the spatial location of potential landslides, providing targeting data for landslide field investigations, technical support for rapid early landslide identification, and data support for landslide management and prevention in Lanzhou.

Risk assessment of debris flow along the northern line of the Sichuan-Tibet highway

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Statistical analysis of the characteristics of typhoons approaching Japan from 2006 to 2019

Abstract: This study investigated the characteristics of 60 typhoons approaching Japan over the past 14 years (2006–2019) by conducting statistical analysis of their temporal evolution, active hours, intensity, frequency, size, duration, and translation speed. By dividing the time period into the earlier (before 2012) and the most recent (after 2013) years, the analysis indicated that the annual frequency of typhoons is higher in the most recent years than in the earlier years. The typhoons in recent years took relatively less time to reach Japan and remained active for shorter time over the land are of Japan than those in the earlier years. The intensity of the typhoons in the recent years showed stronger winds and considerably lower pressures at the landfall time than that in the earlier years. Typhoons in recent years carry more frequent and intense rainfall compared to those in the earlier years in this study. The analysis inferred that the higher sea surface temperature, weaker vertical wind shears, and a larger amount of moisture around the centers of the recent typhoons were responsible for making them stronger.

An integrated approach based landslide susceptibility mapping: case of Muzaffarabad region, Pakistan

Abstract: Landslides result in the devastation of property and loss of lives. This study assesses landslide susceptibility by employing geographic information systems (GIS) and machine learning techniques, that is, support vector machine (SVM) and artificial neural network (ANN), with the integration of advanced optimization techniques, that is, particle swarm optimization (PSO). The landslide-inducing factors considered in this study include fault density, lithology, road density, slope, elevation, flow direction, aspect, earthquake intensity, curvature, Normalized Difference Water Index (NDWI), waterways density, rainfall, and Normalized Difference Vegetation Index (NDVI). The resulting landslide susceptibility maps (LSMs) showed that the areas falling under the high and very high susceptibility class have higher rainfall levels, weak lithology, high NDWI, and flow direction. The accuracy assessment of the techniques showed that ANN with an Area Under the Curve (AUC) of 0.81 performed better than SVM with an AUC of 0.78 without the optimization. Similarly, the performance of ANN was also better than SVM using PSO. During the integrated modeling, the AUC of PSO-ANN was 0.87, whereas the AUC of PSO–SVM was 0.84. The accuracy assessment of the produced LSMs also showed a similar trend in terms of accuracy percentage as that of the models.

Seismic vulnerability and risk assessment at the urban scale using support vector machine and GIScience technology: a case study of the Lixia District in Jinan City, China

Abstract: The increase in the number and severity of seismic disasters has put communities in danger, especially in rapidly developing and densely populated areas. Traditional seismic vulnerability and risk assessment methods, including field investigation, are accurate at the building scale; however, their low-efficiency and high-cost characteristics limit the application of these methods in urban-scale regions with high-speed development and risk exposure. To address this issue, multisource remote sensing interpretation, support vector machine (SVM) and geographic information science (GIScience) technologies are combined to test the performance and efficiency of a urban-scale macroscopic seismic vulnerability and risk assessment method in the Lixia District of Jinan City, Shandong Province, China, which is characterized by rapid development, a variety of building types, and moderate-to-low seismic risk. First, a traditional field survey was conducted in Lixia District, and a building attribute information database was constructed. Second, the vulnerability proxies of building attribute information and building seismic vulnerability were estimated based on the EMS-98 standard and the SVM. Finally, vulnerability proxies established based on the RISK-UE model were applied to the Lixia database, and the vulnerability and risk assessment under different seismic intensities were estimated with the experimental accuracy verified. The results showed that the SVM method can obtain stable and accurate results in urban scale vulnerability assessment. The mean building vulnerability index in Lixia District is 0.43, which indicates that the overall seismic performance is good. Most of the area falls within the seismic intensity range of VII-X degrees and would experience slight to moderate damage. The results of the study contribute to enhancing the precision and efficacy of large-scale seismic risk assessment, and they can be used by relevant departments to create tailored emergency plans and reduce seismic hazard losses. Additionally, these results can aid in achieving the climate action goal (SDG13) of the United Nations Sustainable Development Goals (SDGs).

Influence of the extra-thick coal seam exploitation on the deformation characteristics of the overlying rock mass in an open-pit mine slope

Abstract: Coal seam mining causes movement and deformation of the overlying rock mass and ground surface, directly resulting in serious damage to the surface construction facilities. To investigate the deformation mechanism and characteristics of the overlying rock mass, three-dimensional numerical simulation and model test are carried out on an open-pit mine slope. The results show that the sliding instability characteristics of the slope can be identified based on the numerical analysis. Coal mining affects on the deformation characteristics of the overlying rock mass near the inner dump. The closer to the slope boundary, the greater the subsidence of overlying rock mass. The maximum displacement occurs near the inner dump. The slope mining has great influence on the deformation characteristics of inner dump. Moreover, the damage process of overlying rock mass can be further studied via the model test. After coal seams mining out, the overlying rock mass occurs overall settlement failure. There is a large difference between the lower end side of working face and the upper end side at the caving angle of roof. This work can provide a basis for the safe operation of coal mine disaster prevention and mitigation.

A spatial evaluation method for earthquake disaster using optimized BP neural network model

Abstract: Rapid spatial evaluation of seismic disaster after earthquake occurrence is required in disaster emergency rescue management, because of its importance in decreasing casualties and property losses. Among many categories of seismic disaster, evaluation of earthquake-affected population is of great significance to clarify the severity of earthquake disaster. For simple classic regression model, it is difficult to describe the strong nonlinear relationship between multiple influencing factors and earthquake disasters. In present study, an optimized BP neural network model considering spatial characteristic of influencing factors is proposed to evaluate the population distribution affected by earthquake. The correlation between earthquake-affected population and influencing factors is analysed using data of 2013 Ms7.0 Lushan earthquake. Ten influencing factors including elevation, slope angle, population density, per capita GDP, distance to fault, distance to river, NDVI, PGA, PGV, and distance to the epicentre, were classified into environmental and seismic factors. Correlation analysis revealed that per capita GDP and PGA factor had a stronger correlation with the earthquake-affected population. The earthquake-affected population was evaluated using a BP neural network by optimizing training samples considering spatial characteristics of per capita GDP and PGA factors. Different numbers of sample points, instead of a random distribution of sample points, were generated in areas with different value intervals of the influencing factors. The optimized samples improved the convergence speed and generalization capability of neuron network compared to random samples. The trained network was applied to the 2017 Ms7.0 Jiuzhaigou earthquake to verify its prediction accuracy. The MAE of the estimated earthquake-affected populations of different counties under Jiuzhaigou earthquake were 1.276 people/km2 using network model from optimized samples, smaller than the results of network model from random samples and linear regression model. The results indicate that BP neural network, which considers correlation characteristics of factors, has capability to evaluate spatial earthquake disaster.

Investigation on the spatial distribution of landslides in Sichuan Province, southwest China

Abstract: In this study, a total of 34,893 landslides collected from the Sichuan Province of China are used to reveal their spatial distribution. Correlations between the spatial distribution of landslides and five main factors [i.e. topography, rainfall, lithology, peak ground acceleration (PGA), and human activity] are analyzed based on the Geographical Information System (GIS) platform. Topographic factors exert a significant influence on landslide occurrence, and other factors also sensitive to landslides include lithology (Classes 3 and 4), PGA (≤0.15 g), rainfall (90–140 mm), and road density (≤0.6 km/km2). The geographical detector model is used to detect the relative importance of individual influencing factors and the factor interaction effects on the landslide distribution. The results show that the interacting factors between rainfall and PGA, lithology, river density, and road density are more closely related to the spatial distribution of landslides than every single factor. The results of this study can provide a foundation and insights for hazard reduction schemes in the future.

Effect of initiation pattern on rock damage and blasting seismic under multi-hole blasting

Abstract: In multi-hole blasting, the design of the initiation pattern is critical for the blasting effectiveness, but its effects on rock damage and blasting seismic are still unclear. This research focused on investigating the effect of initiation patterns under multi-hole blasting using numerical simulations and in-situ blasting experiments. First, the rock damage mode and blasting seismic characteristics under four initiation patterns were numerically studied. The rock damage degree was 19.26%, 13.42%, 17.06%, and 9.17% for the four initiation patterns, respectively. Correspondingly, the peak particle velocity (PPV) at monitoring point A reached 2.90 m/s in Case 1, but only 1.56–1.69 m/s in the other three cases. The results indicated the simultaneous initiation pattern causes more severe damage to the rock between blastholes and enhances blasting seismic effect. Furthermore, when the blasthole with a big explosive charge was preferentially initiated, the outcome of rock damage was improved. In-situ blasting experiments with various initiation patterns were conducted in a coal mine, and the blasting seismic was measured. The blasting seismic intensity is closely related to the blastholes number, explosive charge and spacing of blastholes. The simultaneous initiation pattern enhances both the PPV and source energy of blasting seismic, potentially causing rock bursts in the underground roadway. Finally, the practicable initiation pattern of deep-hole blasting in the coal mine was suggested.

Water-sand inrush risk assessment method of sandy dolomite tunnel and its application in the Chenaju tunnel, southwest of China

Abstract: Sandy dolomite strata are widely distributed in the diversion tunnel of the Yuxi section of the Water Diversion Project in Central Yunnan (WDPCY). The water-sand inrush is the main failure phenomenon in the construction of sandy dolomite tunnel, which has seriously and commonly threatened the safety of the Chenaju (CNJ) tunnel in the WDPCY. In this paper, we established the discriminant matrix of the water-sand inrush risk assessment by consulting the experts, including the designers and construction personnel of the CNJ tunnel. Then, the risk assessment of water-sand inrush in the CNJ tunnel is carried out based on the proposed Fuzzy-AHP method. The results show that (1) a hierarchical model for risk assessment of water-sand inrush in sandy dolomite tunnel is constructed by criterion and index including thirteen factors. (2) The main criterion layer factors affecting water-sand inrush in the sandy dolomite tunnel are dolomite’s sandy degree and water yield. (3) The risk grade of water-sand inrush in the sections of YX49 + 089∼YX49 + 099 and YX51 + 637∼YX51 + 647 in the CNJ tunnel is V, which indicates very high risk. The assessment results are consistent with the actual engineering situation, which indicate the proposed method can provide the reference for the risk management in a similar project.Key Policy HighlightsThirteen main factors induce water-sand inrush in sandy dolomite tunnel.Characteristics of sandy dolomite mass and groundwater condition play a decisive role in sandy dolomite tunnel for water-sand inrush.A water-sand inrush risk assessment system established based on Fuzzy-AHP method is suitable for sandy dolomite tunnel.

Water depth estimation from Sentinel-2 imagery using advanced machine learning methods and explainable artificial intelligence

Abstract: The estimation of water depth in coastal areas and shallow waters is crucial for marine management and monitoring. However, direct measurements using fieldwork methods can be costly and time-consuming. Therefore, remote sensing imagery is a promising source of geospatial information for coastal planning and development. To this end, this study investigates advanced machine learning (ML) methods and redesigned morphological profiles for water depth estimation using high-resolution Sentinel-2 satellite imagery. The proposed framework involves three main steps: (1) morphological feature generation, (2) model training using several ML methods (Decision Tree, Random Forest, eXtreme Gradient BOOSTing, Light Gradient Boosting Machine, Deep Neural Network, and CatBoost), and (3) model interpretation using eXplainable Artificial Intelligence (XAI). The performance of the proposed method was evaluated in two different coastal areas (port and jetty) with reference data from accurate hydrographic data (Echo-sounder and differential global positioning systems). The statistical analysis revealed that the proposed method had high efficiency for depth estimation of the coastal area, achieving a best R2 value of 0.96 and Root Mean Square Error (RMSE) of 0.27 m in water depth estimation in the shallow water of Chabahar Bay in the Oman Sea. Additionally, the higher impact and interaction of the morphological features were verified using XAI for water depth mapping.

Research on failure mechanism and support technology of fractured rock mass in an undersea gold mine

Abstract: The surrounding rock control has been a difficult problem for fractured rock mass in hard rock mines. This article describes a case study of the failure mechanisms and the support design technology for fractured rock mass drifts in Xinli Gold Mine. Based on field investigation, the geology characteristics, failure types, influencing factors, support types, and their failure types were analyzed. The rock mass classification, rock mass physical and mechanical parameters were obtained by using Q, RMR, and GSI systems. The zoning of surrounding rock, stability analysis and zoning support schemes design were carried out based on rock mass classification results. The pretension is designed by China underground mine experiences and verified by numerical simulation. RS2 was used to compare the plastic zone under pre- and post-support conditions. The plastic zone is significantly reduced after support is installed, which indicates that the designed support schemes can effectively control the failure of surrounding rock. In view of difficulties in the excavation and support of fractured rock mass, the short excavation and short support technology was proposed to ensure the success excavation of the drift in fractured rock mass. The field application shows that the short excavation and support technology are effective.

Investigating two-dimensional deformations of the head of the Central Route of the South–North Water Diversion Project in China with TerraSAR-X datasets

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