Disasters can have devastating impacts on communities and economies, underscoring the urgent need for effective strategic disaster risk management (DRM). Although Artificial Intelligence (AI) holds the potential to enhance DRM through improved decision-making processes, its inherent complexity and "black box" nature have led to a growing demand for Explainable AI (XAI) techniques. These techniques facilitate the interpretation and understanding of decisions made by AI models, promoting transparency and trust. However, the current state of XAI applications in DRM, their achievements, and the challenges they face remain underexplored. In this systematic literature review, we delve into the burgeoning domain of XAI-DRM, extracting 195 publications from the Scopus and ISI Web of Knowledge databases, and selecting 68 for detailed analysis based on predefined exclusion criteria. Our study addresses pertinent research questions, identifies various hazard and disaster types, risk components, and AI and XAI methods, uncovers the inherent challenges and limitations of these approaches, and provides synthesized insights to enhance their explainability and effectiveness in disaster decision-making. Notably, we observed a significant increase in the use of XAI techniques for DRM in 2022 and 2023, emphasizing the growing need for transparency and interpretability. Through a rigorous methodology, we offer key research directions that can serve as a guide for future studies. Our recommendations highlight the importance of multi-hazard risk analysis, the integration of XAI in early warning systems and digital twins, and the incorporation of causal inference methods to enhance DRM strategy planning and effectiveness. This study serves as a beacon for researchers and practitioners alike, illuminating the intricate interplay between XAI and DRM, and revealing the profound potential of AI solutions in revolutionizing disaster risk management. 

Explainable artificial intelligence in disaster risk management: Achievements and prospective futures

Studying the relationship between human activities and soil erosion on a regional scale is of great significance for macro-decision-making in soil erosion prevention and control. The entropy weight method and RUSLE model are used to analyze the spatiotemporal variation in human activity intensity (HAI) and soil erosion in the Weihe River Basin (WRB) from 2005 to 2020. Through geographic detectors and a four-quadrant model, the impact of various driving factors and the coupling degree of the human–land relationship are studied. The results showed: (1) During the past 15 years, the moderate, high, strong, and severe erosion areas in the WRB decreased by 9.88%, 35.89%, 45.17%, and 70.05%, respectively. The ratio of the historical sand transport modulus to the RUSLE model result is 0.83, indicating that the results obtained by the RUSLE model can be used for further analysis. (2) Slight and weak degrees account for 80% in the northwest region. The high and strong regions are mainly distributed in the Shaanxi section, accounting for 3% of the total basin. (3) The coupling between human activities and soil erosion is constantly strengthening, and the joint effect of pop and crop is the main reason for the slowdown and spatial differences in soil erosion. This indicates that the ecological environment became stable. These findings contribute by acting as references for soil and water conservation and management in the WRB to promote a harmonious relationship between humans and the environment.

https://www.mdpi.com/2071-1050/15/14/10785/pdf?version=1688982641

Spatiotemporal Variation in the Coupling Relationship between Human Activities and Soil Erosion—A Case Study in the Weihe River Basin

Consumer goods volume and their consumption structure significantly contribute to water pollution within economic zones. However, there remains a need to better comprehend the nuanced and seasonal nonlinear relationship between consumer goods and water pollution, which currently obstructs essential consumption adjustments aimed at reducing water pollution. This study selected an effective machine learning model Light Gradient Boosting Machine (LightGBM) with SHapley Additive Explanations (SHAP) to assess the nonlinear relationship between consumer goods and water pollution during flood and non-flood seasons within the Yangtze River Economic Belt from 2013 to 2022, Baidu Index of consumer goods were used to represent the potential consumption level (PCL). The findings revealed the following: (1) During flood seasons, hydroelectricity and the PCL of furniture and cultural goods emerged as factors driving water pollution reduction. Conversely, GDP, PCL of communication devices, and beverages played significant roles in exacerbating water pollution. During non-flood seasons, the drivers for reducing water pollution included GDP, income gap and PCL of cultural goods. Meanwhile, PCL of cars, western medicine and oil became pivotal drivers for exacerbating water pollution. (2) Concerning consumption structure during flood and non-flood seasons, maintaining consumption levels of costume industrial goods, cultural, sports and health goods and transportation and accommodation goods below 66.71%, 77.66% and 79.61% respectively of their average PCL appeared beneficial in reducing water pollution. During flood seasons, consumption levels of cars and furniture below 85.69% and 91.46% respectively of their average PCL were associated with reduced water pollution. During non-flood seasons, consumption levels of cars and oil below 82.95% and 71.28% respectively of their average PCL could help reduce water pollution. (3) The proportion of hydroelectricity generation exceeding 2% and 3% respectively during flood and non-flood seasons helped in reducing water pollution. Moreover, GDP per capita surpassing 2388.82 and 2075.78 respectively during flood and non-flood season was preferable for reducing water pollution. These findings deepened our understanding of the nonlinear relationship and identify promising consumer goods and their consumption structure that could effectively aid in reducing water pollution from a consumption perspective. 

Assessing the nonlinear relationship between consumer goods and water pollution in different seasons with machine learning models: A case study in the Yangtze River Economic Belt

Coupling coordination degree measurement and spatial characteristics analysis of green finance and technological innovation -Empirical analysis based on China

Owing to the contradiction between agricultural production and environmental development, the issues of food security and carbon mitigation cannot be isolated, and achieving coupled and coordinated development is the key to agricultural sustainability. This study adopted the coupled coordination model and dynamic qualitative comparative analysis (dynamic QCA) method to measure the coupled coordination degree (CCD) of the food security index (FSI) and agricultural carbon emission efficiency (ACEE) in 31 provinces of China from 2010 to 2021, seeking paths to achieve high coupled coordination from Climate-Smart Agriculture technology, external environment, and incentive dimensions, and simulating path selection differences under various CSA priority scenarios. The results indicated that the CCD of the FSI and ACEE in China significantly increased year-on-year increase, with significant regional differences primarily reflected in the Northeast > East > West > Central regions. Based on the CSA orientation, the "technology-environmental safeguard" linkage path and the "technology-environment-incentive" hybrid path are proposed. There are differences in CSA practices across regions, which require customization based on their unique socioeconomic, ecological, and political landscapes. When priorities favour food security, the "technology-environment-incentive" hybrid pathway supports high CCD, and as priorities increase, the contribution of CSA technologies, centred on water-saving irrigation, increases and the role of the external environment diminishes. When the priority tendency is to mitigate emissions, both paths can achieve high CCD. As the priority tendency for carbon emissions increases, urbanisation and CSA technologies such as water-saving irrigation and straw return become essential factors contributing to higher coupling coordination, and the role of agriculture-related financial expenditures diminishes. These findings provide policy support for safeguarding food security and low-carbon agriculture. 

Coupled coordination and pathway analysis of food security and carbon emission efficiency under climate-smart agriculture orientation

Factors affecting phase change in coupling coordination between population, crop yield, and soil erosion in China’s 281 cities

On basis of the slurry preparation of diaphragm wall construction in Jinshan Metro Station in Fuzhou, firstly, laboratory tests are conducted to obtain 3D characteristic figures which exhibit the effects of silt soil and bentonite content on slurry properties including density, viscosity and water loss. Furthermore, influence of slurry density, viscosity on filter cake forming process under different slurry pressure is analyzed through laboratory tests. The test results indicate that: the 3D characteristic figures can give a variety of slurry proportion combinations of specific density, viscosity and water loss. The final unit water filtration first drops then starts to increase as slurry density increases. As for viscosity and slurry pressure, the final unit water filtration decreases as viscosity increases and it increases as slurry pressure increases. In addition, the influence of viscosity on final unit water filtration is less than that of density. Finally, optimization of trench construction is proposed based on filter cake formation time obtaining from the test results and construction conditions.

On Trench Construction of Diaphragm Wall in Medium-Coarse Sand: Slurry Composition and Construction Optimization

The invention relates to a counterforce device for subgrade plate loading test and a field test method. The counterforce device includes a pair of screw support mechanisms respectively in fixed connection with steel rails on both sides and a counterforce beam arranged between the two screw support mechanisms, the bottom surface of the counterforce beam contacts counterforce rods, and provides counterforce for the counterforce rods. The field test method includes: firstly determining an optimal measurement point position scheme, connecting the device with the steel rails after arriving at a measurement point, then adjusting the height of the counterforce beam till contact with the tops of the counterforce rods, and before detection, preloading a pressure to inspect the state of the counterforce beam, and then performing test. Compared with the prior art, the counterforce device provided by the invention can adapt to the construction process of an AHM800R-CH type subgrade treatment vehicle, solves the two problems of insufficient operation space and difficult counterforce device erection in subgrade K30 detection, the counterforce device is rapid in erection and transfer, has little influence on construction, and can realize zero interference on subgrade replacement operation.

Counterforce device for subgrade plate loading test and field test method

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Factors affecting phase change in coupling coordination between population, crop yield, and soil erosion in China’s 281 cities

On Trench Construction of Diaphragm Wall in Medium-Coarse Sand: Slurry Composition and Construction Optimization

Counterforce device for subgrade plate loading test and field test method