Decision-making method based on game theory and grey theory in a single-value neutrosophic environment and its application to typhoon disaster assessment

PurposeThe Chinese believe that “man will conquer the sky” and “fighting with the sky brings endless joy”. Considering that disaster assessment can be regarded as a two-person, zero-sum game problem between nature and human beings, this paper proposes a multi-attribute decision-making method based on game theory and grey theory in a single-value neutrosophic set environment. Due to the complexity and uncertainty of the decision-making environment, the method builds a decision matrix based on single-valued neutrosophic numbers.Design/methodology/approachFirst, the authors use the single-value neutrosophic information entropy to calculate the attribute weights and the weighted decision matrix. Second, the optimal mixed strategy method based on linear programming solves the optimal mixed strategy for both sides of the game so that the expected payoff matrix can be obtained. Finally, grey correlation analysis is used to obtain the closeness coefficient of each alternative based on the expectation payoff matrix to identify the ranking result of the alternative.FindingsAn example is used to verify the effectiveness of the proposed method, and its rationality is verified through a comprehensive comparison and analysis of the various aspects.Practical implicationsThe proposed decision-making method can be applied to typhoon disaster assessment. Such assessment results can provide intelligent decision support to the relevant disaster management departments, thereby reducing the negative impact of typhoon disasters on society, stabilizing society and improving people's happiness. Further, the method can be used for decision-making, recommendation and evaluation in other fields.Originality/valueThe proposed method uses single-value neutrosophic numbers to solve the information representation problem of decision-making in a complex environment. Under a new perspective, game theory is used to handle the decision matrix, while grey relational analysis converts inexact numbers to exact numbers for comparison and sorting. Thus, the proposed method can be used to make reasonable decisions while preserving information to the extent possible.

High-Temperature Disaster Risk Assessment for Urban Communities: A Case Study in Wuhan, China

High-temperature risk disaster, a common meteorological disaster, seriously affects people’s productivity, life, and health. However, insufficient attention has been paid to this disaster in urban communities. To assess the risk of high-temperature disasters, this study, using remote sensing data and geographic information data, analyzes 973 communities in downtown Wuhan with the geography-weighted regression method. First, the study evaluates the distribution characteristics of high temperatures in communities and explores the spatial differences of risks. Second, a metrics and weight system is constructed, from which the main factors are determined. Third, a risk assessment model of high-temperature disasters is established from disaster-causing danger, disaster-generating sensitivity, and disaster-bearing vulnerability. The results show that: (a) the significance of the impact of the built environment on high-temperature disasters is obviously different from its coefficient space differentiation; (b) the risk in the old city is high, whereas that in the area around the river is low; and (c) different risk areas should design built environment optimization strategies aimed specifically at the area. The significance of this study is that it develops a high-temperature disaster assessment framework for risk identification, impact differentiation, and difference optimization, and provides theoretical support for urban high-temperature disaster prevention and mitigation.

Collaborative Hybrid Aerial and Ground Vehicle Routing for Post-Disaster Assessment

Hybrid aerial and ground vehicles are seen as a promising option for deployment in a post-disaster assessment due to the risk of infrastructure damage that may hinder the assessment operation. The efficient operation of the hybrid aerial and ground vehicle, particularly routings, remains a challenge. The present study proposed a collaborative hybrid aerial and ground vehicle to support the operation of post-disaster assessment. The study developed two models, i.e., the Two-Echelon Vehicle Routing Problem combined with Assignment (2EVRPA) and the Two-Echelon Collaborative Vehicle Routing Problem (2ECoVRP) to evaluate optimal routings for both aerial and ground vehicles. The difference lies in the second echelon in which the 2EVRPA uses a single point-to-point assignment, whereas the 2ECoVRP considers the collaborative routings between the ground vehicle and the aerial vehicle. To demonstrate its applicability, the developed models were applied to solve the post-disaster assessment for the Mount Merapi eruption in Yogyakarta, Indonesia. Sets of numerical experiments based on the empirical case were conducted. The findings indicate that the 2ECoVRP performs better than 2EVRPA in terms of the total operation time. The tabu search algorithm was found to be a promising method to solve the models due to its good quality solution and computational efficiency. The deployment of eight drones appears to be optimum for the given network configuration of the studied case. Flight altitude and battery capacity were found to be influential to the operation time, hence requiring further exploration. Other potential avenues for future research are also discussed.

ASHFALL DISPERSAL MAPPING OF THE 2020 TAAL VOLCANO ERUPTION USING DIWATA-2 IMAGERY FOR DISASTER ASSESSMENT

Natural disasters incur many fatalities and economic losses for vulnerable and developing countries such as the Philippines. It is crucial that during calamities, on-ground surveillance is supplemented by low-cost and time-efficient methods such as satellite remote sensing. Diwata-2 is a Philippine microsatellite specifically equipped for disaster assessment. In this study, the capabilities of this satellite in ashfall detection were explored by closely examining the case of the Taal volcano eruption on January 12, 2020. Satellite images covering parts of CALABARZON and Metropolitan Manila before and after the phreatomagmatic eruption were compared. The presence and extent of heavy ash over the study area were identified after the image classification using the Support Vector Machine (SVM) algorithm. A decrease in vegetation cover and built-up areas was also observed. Upon validation, an overall accuracy of 91.4562 and Kappa coefficient of 0.8833 were achieved for the post-eruption ashfall extent map, exhibiting the potential of Diwata-2 imagery in monitoring volcanic eruptions and similar phenomena.

Detecting and Analyzing the Displacement of a Small-Magnitude Earthquake Cluster in Rong County, China by the GACOS Based InSAR Technology

A series of small-magnitude earthquakes (Mw 2.9~Mw 4.9) occurred in Rong County, Sichuan Province, China between 30 March 2018 and December 2020, which threatened the safety of local residents. Determining the surface displacement and estimating the damage caused by these earthquakes are significant for earthquake relief, post-earthquake disaster assessment and hazard elimination. This paper integrates the Generic Atmospheric Correction Online Service (GACOS) with interferometry synthetic aperture radar (InSAR) to accurately detect the displacement of the series of small-magnitude earthquakes in Rong County based on 45 Sentinel-1 ascending/descending images acquired from January 2018 to December 2020. We analyze the influence of some factors involved in surface displacement, including earthquake magnitude, focal depth and the distance from the epicenter to the fault. The above measurement for small-magnitude earthquakes and statistics analysis for the displacement have not been performed before, so this can help better understand the displacement features of small-magnitude earthquakes, which are important for post-earthquake hazard assessment and disaster prevention.

Accurate Wheat Lodging Extraction from Multi-Channel UAV Images Using a Lightweight Network Model

The extraction of wheat lodging is of great significance to post-disaster agricultural production management, disaster assessment and insurance subsidies. At present, the recognition of lodging wheat in the actual complex field environment still has low accuracy and poor real-time performance. To overcome this gap, first, four-channel fusion images, including RGB and DSM (digital surface model), as well as RGB and ExG (excess green), were constructed based on the RGB image acquired from unmanned aerial vehicle (UAV). Second, a Mobile U-Net model that combined a lightweight neural network with a depthwise separable convolution and U-Net model was proposed. Finally, three data sets (RGB, RGB + DSM and RGB + ExG) were used to train, verify, test and evaluate the proposed model. The results of the experiment showed that the overall accuracy of lodging recognition based on RGB + DSM reached 88.99%, which is 11.8% higher than that of original RGB and 6.2% higher than that of RGB + ExG. In addition, our proposed model was superior to typical deep learning frameworks in terms of model parameters, processing speed and segmentation accuracy. The optimized Mobile U-Net model reached 9.49 million parameters, which was 27.3% and 33.3% faster than the FCN and U-Net models, respectively. Furthermore, for RGB + DSM wheat lodging extraction, the overall accuracy of Mobile U-Net was improved by 24.3% and 15.3% compared with FCN and U-Net, respectively. Therefore, the Mobile U-Net model using RGB + DSM could extract wheat lodging with higher accuracy, fewer parameters and stronger robustness.

Global Runoff Signatures Changes and Their Response to Atmospheric Environment, GRACE Water Storage, and Dams

Runoff signatures (RS), a special set of runoff indexes reflecting the hydrological process, have an important influence on many fields of both human and natural systems by flooding, drought, and available water resources. However, the global RS changes and their causes remain largely unknown. Here, we make a comprehensive investigation of RS changes and their response to total water storage anomalies (TWSA) from GRACE satellites, atmospheric circulation, and reservoir construction by using daily runoff data from 21,955 hydrological stations during 1975–2017. The global assessment shows that (1) in recent years, the global extreme flow signatures tend to decrease, while the low and average flow signatures are likely to increase in more regions; (2) the spatial patterns of trends are similar for different RS, suggesting that the runoff distribution tends to entirely upward in some regions, while downward in other regions; (3) the trends in RS are largely consistent with that in TWSA over most regions in North America and eastern South America during 1979–2017, indicating that the GRACE-based TWSA have great potential in hydrological monitoring and attribution; (4) atmospheric circulation change could partly explain the global spatiotemporal variation patterns of RS; (5) dams have important influences on reducing the high flow signature in the catchments including dams built during 1975–2017. This study provides a full picture of RS changes and their possible causes, which has important implications for water resources management and flood and drought disaster assessment.

Robust minimum cost consensus models with aggregation operators under individual opinion uncertainty

Individual opinion is one of the vital factors influencing the consensus in group decision-making, and is often uncertain. The previous studies mostly used probability distribution, interval distribution or uncertainty distribution function to describe the uncertainty of individual opinions. However, this requires an accurate understanding of the individual opinions distribution, which is often difficult to satisfy in real life. In order to overcome this shortcoming, this paper uses a robust optimization method to construct three uncertain sets to better characterize the uncertainty of individual initial opinions. In addition, we used three different aggregation operators to obtain collective opinions instead of using fixed values. Furthermore, we applied the numerical simulations on flood disaster assessment in south China so as to evaluate the robustness of the solutions obtained by the robust consensus models that we proposed. The results showed that the proposed models are more robust than the previous models. Finally, the sensitivity analysis of uncertain parameters was discussed and compared, and the characteristics of the proposed models were revealed.

Disaster Image Classification by Fusing Multimodal Social Media Data

Social media datasets have been widely used in disaster assessment and management. When a disaster occurs, many users post messages in a variety of formats, e.g., image and text, on social media platforms. Useful information could be mined from these multimodal data to enable situational awareness and to support decision making during disasters. However, the multimodal data collected from social media contain a lot of irrelevant and misleading content that needs to be filtered out. Existing work has mostly used unimodal methods to classify disaster messages. In other words, these methods treated the image and textual features separately. While a few methods adopted multimodality to deal with the data, their accuracy cannot be guaranteed. This research seamlessly integrates image and text information by developing a multimodal fusion approach to identify useful disaster images collected from social media platforms. In particular, a deep learning method is used to extract the visual features from social media, and a FastText framework is then used to extract the textual features. Next, a novel data fusion model is developed to combine both visual and textual features to classify relevant disaster images. Experiments on a real-world disaster dataset, CrisisMMD, are performed, and the validation results demonstrate that the method consistently and significantly outperforms the previously published state-of-the-art work by over 3%, with a performance improvement from 84.4% to 87.6%.