Earthquake-induced landslides (EQILs) are an incredibly destructive geological disaster. Rapid landslide susceptibility assessments are indispensable and critical for risk analysis and emergency ...management. Previous studies mainly focus on the regional-scale assessment of EQIL susceptibility, while the global analyses of that are lacking. In this study, we constructed a global model for rapidly assessing earthquake-induced landslide susceptibility based on the random forest (RF) algorithm using globally available data. In total, 288,114 landslides from 16 high-quality EQIL inventories were utilized to develop the global landslide model. We split the data into 70% training dataset for model training and 30% testing data for model evaluation. We also used three blind test events to validate the model performance. The model showed excellent performance on the testing data (accuracy = 0.945, and AUC = 0.985). The RF model exhibited strong spatial generalizability and robustness, with an AUC exceeding 0.8 for each landslide inventory and showing good performance on the blind test events. The resulting landslide susceptibility maps also match relatively well with the actual landslide locations. Among the conditioning factors, modified Mercalli intensity (MMI), elevation and slope are the three most important conditioning factors. The susceptibility maps for each landslide event were produced. The developed RF model would be useful in studies of earthquake-induced landslide susceptibility and emergency response after an earthquake.
•A global random forest model assessing earthquake-induced landslide susceptibility.•Performances of the model are good in 16 landslide inventories and blind testing.•Modified Mercalli intensity, elevation and slope are the most important factors.
Rapid assessment methods (RAMs) have become an integral part of state and federal wetland programs by providing a consistent method for monitoring and prioritizing wetland conservation efforts. RAMs ...evaluate condition along an anthropogenic disturbance gradient based on qualitative and quantitative measures of wetland indicators. However, RAM applicability outside of the intended region may be difficult or inappropriate due to differences in wetland types, natural variability, and types of stressors. Given the influence of regional and wetland variability on the effectiveness of RAMs, our approach focused on the development and validation of a method applicable to specific wetland types found in Oklahoma and other regions in the Central Great Plains. We applied the Oklahoma Rapid Assessment Method (OKRAM) in 28 depressional wetlands across the state and evaluated the method’s ability to detect condition along a disturbance gradient. We found consistent relationships between OKRAM scores and plant data (e.g., Floristic Quality Index, species richness, and diversity) and with a landscape assessment of anthropogenic disturbance. Based on our results, OKRAM has utility as a tool for differentiating between high and low quality depressional wetlands in Oklahoma, with potential for applicability across other regions of the Central Great Plains.
Aim
The incidence of major fires is increasing globally, creating extraordinary challenges for governments, managers and conservation scientists. In 2019–2020, Australia experienced precedent‐setting ...fires that burned over several months, affecting seven states and territories and causing massive biodiversity loss. Whilst the fires were still burning, the Australian Government convened a biodiversity Expert Panel to guide its bushfire response. A pressing need was to target emergency investment and management to reduce the chance of extinctions and maximise the chances of longer‐term recovery. We describe the approach taken to rapidly prioritise fire‐affected animal species. We use the experience to consider the organisational and data requirements for evidence‐based responses to future ecological disasters.
Location
Forested biomes of subtropical and temperate Australia, with lessons for other regions.
Methods
We developed assessment frameworks to screen fire‐affected species based on their pre‐fire conservation status, the proportion of their distribution overlapping with fires, and their behavioural/ecological traits relating to fire vulnerability. Using formal and informal networks of scientists, government and non‐government staff and managers, we collated expert input and data from multiple sources, undertook the analyses, and completed the assessments in 3 weeks for vertebrates and 8 weeks for invertebrates.
Results
The assessments prioritised 92 vertebrate and 213 invertebrate species for urgent management response; another 147 invertebrate species were placed on a watchlist requiring further information.
Conclusions
The priority species lists helped focus government and non‐government investment, management and research effort, and communication to the public. Using multiple expert networks allowed the assessments to be completed rapidly using the best information available. However, the assessments highlighted substantial gaps in data availability and access, deficiencies in statutory threatened species listings, and the need for capacity‐building across the conservation science and management sectors. We outline a flexible template for using evidence effectively in emergency responses for future ecological disasters.
•An improved minimal cut set algorithm based on fault trees was built.•A dual-function substation network model based on structure and electricity was proposed.•A set of rapid earthquake risk ...assessment method for substations was given.•A set of earthquake risk reduction strategies was obtained through a multi-level analysis.
Electrical substation systems have been severely damaged in previous earthquakes. It is important to realize the rapid seismic risk assessment of substation systems before an earthquake occurs. This paper proposes a rapid seismic risk assessment framework based on seismic hazards and a functional network model. According to the structural connectivity of the substation, the series and parallel connections of equipment were clarified. Considering the power load capacity of equipment and the power transmission requirements of systems, a dual-functional network model combining structure and electricity was built. On the premise of ensuring earthquake risk probability information, the hierarchical simulation of the fault tree model was carried out. Also, the minimum cut set algorithm was used to rapidly evaluate the seismic risk of the substation system. Aiming at reducing the risk of substation function failure, two risk reduction strategies of seismic retrofitting and additional redundant equipment were put forward. After the seismic analysis of a typical 220 kV substation, key equipment of the substation and the optimal risk reduction strategies were obtained.
Underwater explosions can cause significant damage to ship structures, and quickly assessing the extent of the damage is crucial for improving warship combat capability. This paper proposes the use ...of machine learning algorithms to rapidly assess the damage of stiffened plates subjected to underwater explosions. The algorithms use structural responses of the plates obtained by numerical simulations, which are benchmarked by experimental results, as a database. Fractures and plastic deformations are both taken into consideration. The support vector machine algorithm is used to determine the criterion for fractures or plastic deformations, while a back propagation neural network model and a support vector regression model are both used to predict the plastic deformation and fracture area of the plates. The support vector machine model accurately classified different cases of fractures or plastic deformation with a training accuracy of 99.4%. The back propagation neural network model has regression values of 0.99 for predicting fractures and 0.97 for predicting plastic deformation, both of which are higher than those predicted by the support vector regression model (0.96 for the prediction of fracture and 0.90 for the prediction of plastic deformation). Therefore, the back propagation neural network model provides a more accurate assessment of damage to stiffened plates subjected to underwater explosions and can be used for rapid assessment.
•Machine learning algorithms are employed to predict the structural response.•The responses of stiffened plates subjected to underwater explosions are studied.•The criterion for fractures or plastic deformations is determine by the SVM model.•The BP neural network model and the SVR model are compared.•Rapid assessment of fractures or plastic deformation can be achieved.
•The study presents a novel model to conduct rapid assessment applied to Temuco, Chile.•Results show that a total 55 MW DH capacity is required to cover the heating demand.•The model estimates an ...annual reduction of 24382 t of PM10 and 23692 t of PM2.5.•The model is an effective tool to perform rapid assessment of DH projects in Chile.•Attractive projects can move forward to more detailed pre-feasibility analysis.
District energy systems (DES) offer an optimal solution for decarbonising the heating and cooling sector while attaining multiple additional benefits. The first step to analyse the potential of DES in both new and existing markets is through rapid assessments (RA). Currently, publicly available models lack rapid assessments of the technical-economic and environmental potential of DES. This RA model was developed within the framework of UNEP's District Energy in Cities Initiative to identify DES's potential spending low time and monetary resources. In this light, the study presents a model for conducting a rapid assessment applied to the case of Temuco, Chile. Results show that a total of 55 MW DH (district heating) capacity is required to cover the heating demand. A wood-chip boiler of 25 MW capacity and a gas boiler of 30 MW capacity are considered in the calculations. The total CAPEX of the project is around 25 billion CLP, with a NPV of 10.5 billion CLP and an IRR of 14%. The project is also estimated to achieve an annual reduction of 24,382 tons of PM10 and 23,692 tons of PM2.5. The model was validated against an independent study conducted by an international consulting company, and the results were found to be in close proximity with the study. Thus, the model can be an effective tool for performing rapid assessments of DES projects in the region and subjecting attractive projects to more detailed pre-feasibility analysis.
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The COVID-19 global pandemic has had severe, unpredictable and synchronous impacts on all levels of perishable food supply chains (PFSC), across multiple sectors and spatial scales. Aquaculture plays ...a vital and rapidly expanding role in food security, in some cases overtaking wild caught fisheries in the production of high-quality animal protein in this PFSC. We performed a rapid global assessment to evaluate the effects of the COVID-19 pandemic and related emerging control measures on the aquaculture supply chain. Socio-economic effects of the pandemic were analysed by surveying the perceptions of stakeholders, who were asked to describe potential supply-side disruption, vulnerabilities and resilience patterns along the production pipeline with four main supply chain components: a) hatchery, b) production/processing, c) distribution/logistics and d) market. We also assessed different farming strategies, comparing land- vs. sea-based systems; extensive vs. intensive methods; and with and without integrated multi-trophic aquaculture, IMTA. In addition to evaluating levels and sources of economic distress, interviewees were asked to identify mitigation solutions adopted at local / internal (i.e., farm-site) scales, and to express their preference on national / external scale mitigation measures among a set of a priori options. Survey responses identified the potential causes of disruption, ripple effects, sources of food insecurity, and socio-economic conflicts. They also pointed to various levels of mitigation strategies. The collated evidence represents a first baseline useful to address future disaster-driven responses, to reinforce the resilience of the sector and to facilitate the design reconstruction plans and mitigation measures, such as financial aid strategies.
•Rapid global assessment of COVID-19 control measures effects.•Stakeholders perceptions survey on socio-economic effects of the pandemic.•Aquaculture supply chain: hatchery, production/processing, distribution, marketing.•Supply-side disruption, vulnerabilities and resilience patterns were highlighted.•Adopted and preferred mitigation solutions at internal and external scale.
Background: Rapid Assessment of Avoidable Blindness (RAAB) relies mainly on direct ophthalmoscopy (DO) for the diagnosis of posterior segment eye diseases (PSEDs). There is, however, a growing ...concern that DO may not be sufficiently sensitive to detect PSED owing to inherent diagnostic limitations. Aim of the Study: The aim of the study was to determine the sensitivity and specificity of DO for the detection of PSEDs in RAAB, using indirect ophthalmoscopy (IO) as a reference standard. Materials and Methods: Participants were patients 50 years and older, presenting to the eye clinic of a tertiary hospital in Jos, between April and September 2016 who gave consent. Their visual acuity was assessed as is done in RAAB6. Those found to be unilaterally or bilaterally visually impaired underwent anterior and posterior segment eye examinations to identify the cause. Dilated fundoscopy was first performed by a senior ophthalmology resident with DO (index test), followed by IO (reference standard test) by a consultant ophthalmologist. Results: A total of 250 patients were recruited into the study, of which 188 took the index and reference standard tests. PSEDs were detected in 65 (34.6%) persons and glaucoma accounted for 87.7% of these. The sensitivity of DO for the detection of glaucoma was 95.3%, while the specificity was 91.0%. Sensitivity and specificity for the detection of diabetic retinopathy and central retinal vein occlusion were both 100%. Specificity for the detection of other PSEDs was good even though sensitivity was below acceptable limits. Conclusion: The performance of DO against IO in this study, suggests that it is a satisfactory modality for the detection of the most common PSEDs in RAAB.
Resources for evaluating the ecological outcomes of ecosystem restoration projects are often limited, especially within government‐funded programs. In order to rapidly assess the ecological outcomes ...of wetland restoration, an improved approach has been developed, which was applied in the assessment of the ecological outcomes at nine restoration sites of South Africa's Working for Wetlands program. The sites encompass a diversity of restoration problems and land use contexts. The approach begins by distinguishing hydrogeomorphic (HGM) units, for which ecological condition is assessed and reported for hydrology, geomorphology, and vegetation pre‐ and post‐restoration. These three components are closely linked but, as demonstrated at some of the sites, may respond differentially to restoration interventions. For most HGM units, overall ecological condition was improved by between 10 and 30%, with the greatest contribution of restoration generally being to the hydrology component. Having determined the integrity and costs of the interventions, cost‐effectiveness is then reported in South African Rands per hectare equivalent restored, which was found to vary by more than an order of magnitude across the HGM units assessed. Cost‐effectiveness must be interpreted in the light of the long‐term integrity of the interventions, the site's landscape context, and the contribution of restoration to ecosystem services provision. Some sites may be considerably less cost‐effective than others, but the cost may nonetheless be justified if the sites make key contributions to ecosystem services provision. The study was conducted in the context of a formative evaluation and the findings are envisaged to improve wetland restoration practice.
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