Nowadays, water resource security is becoming increasingly prominent, and this problem is a primary bottleneck restricting China’s future sustainable development. It is difficult to come to a unified conclusion on water resources security, and applications of highly feasible evaluation methods are lacking in practice. In this paper, a novel evaluation methodology is proposed for regional water resources security evaluation. First, water security is divided into two aspects: water quantity security and water quality security. The disposal rate of harmless household garbage, the excellent water resources proportion, and the functional water body loss proportion are creatively considered as indicators of water quality security in the evaluation system. In addition, a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is used to evaluate the water security levels in different regions. For distinguishing the importance of different indicators, a Best–Worst Method (BWM) is employed to calculate the indicator weights, as triangular fuzzy linguistic sets can more flexibly describe the preferences of decision makers (DMs) regarding the indicators; therefore, it is embedded in BWM to determine indicator weights. Moreover, the fuzzy BWM-TOPSIS method is applied to evaluate the water security levels of six regions in North China, a comparison analysis with the equal weight TOPSIS method as well as the fuzzy BWM-AHP method, and a sensitivity analysis for indicator weights are presented to illustrate the effectiveness of this proposed method. Finally, some suggestions based on the evaluation results are given for effective and rational utilization of water resources in North China.

Because the natural disaster system is a very comprehensive and large system, the disaster reduction scheme must rely on risk analysis. Experts’ knowledge and experiences play a critical role in disaster risk assessment. The hesitant fuzzy linguistic preference relation is an effective tool to express experts’ preference information when comparing pairwise alternatives. Owing to the lack of knowledge or a heavy workload, information may be missed in the hesitant fuzzy linguistic preference relation. Thus, an incomplete hesitant fuzzy linguistic preference relation is constructed. In this paper, we firstly discuss some properties of the additive consistent hesitant fuzzy linguistic preference relation. Next, the incomplete hesitant fuzzy linguistic preference relation, the normalized hesitant fuzzy linguistic preference relation, and the acceptable hesitant fuzzy linguistic preference relation are defined. Afterwards, three procedures to estimate the missing information are proposed. The first one deals with the situation in which there are only $n-1$ known judgments involving all the alternatives; the second one is used to estimate the missing information of the hesitant fuzzy linguistic preference relation with more known judgments; while the third procedure is used to deal with ignorance situations in which there is at least one alternative with totally missing information. Furthermore, an algorithm for group decision making with incomplete hesitant fuzzy linguistic preference relations is given. Finally, we illustrate our model with a case study about flood disaster risk evaluation. A comparative analysis is presented to testify the advantage of our method.

This research provides a general methodology for distinguishing disaster-related anti-rumor spreaders from a non-ignorant population base, with strong connections in their social circle. Several important influencing factors are examined and illustrated. User information from the most recent posted microblog content of 3793 Sina Weibo users was collected. Natural language processing (NLP) was used for the sentiment and short text similarity analyses, and four machine learning techniques, i.e., logistic regression (LR), support vector machines (SVM), random forest (RF), and extreme gradient boosting (XGBoost) were compared on different rumor refuting microblogs; after which a valid and robust distinguishing XGBoost model was trained and validated to predict who would retweet disaster-related rumor refuting microblogs. Compared with traditional prediction variables that only access user information, the similarity and sentiment analyses of the most recent user microblog contents were found to significantly improve prediction precision and robustness. The number of user microblogs also proved to be a valuable reference for all samples during the prediction process. This prediction methodology could be possibly more useful for WeChat or Facebook as these have relatively stable closed-loop communication channels, which means that rumors are more likely to be refuted by acquaintances. Therefore, the methodology is going to be further optimized and validated on WeChat-like channels in the future. The novel rumor refuting approach presented in this research harnessed NLP for the user microblog content analysis and then used the analysis results of NLP as additional prediction variables to identify the anti-rumor spreaders. Therefore, compared to previous studies, this study presents a new and effective decision support for rumor countermeasures.

Sponge City research has been attracting extensive attention both in practical and theoretical research field, as the increased threat of flood risk and environmental safety due to urbanization. Varies names of Sponge City prevalent in different countries, which leads to disconnection of literature in the same field of Sponge City. In this paper, a systematic literature mining of Sponge City is presented. A literature analysis system is created, which includes literature export from Web of Sciences and systematic analysis via NoteExpress and CiteSpace. Based on the final document storage which contains 962 articles, general trends are identified. Literature is classified into 9 theme types. Research foci of Sponge City are detected by citation and keywords burst detection. Further, some future research directions of Sponge City are anticipated, including trans-disciplinary approaches, a comprehensive design framework, application of information technology, and case studies of Sponge City in more parts of the world. The significance of this paper lies in summarizing past research, identifying research types, foci and anticipating some future research directions.

One prominent dark side of online information behavior is the spreading of rumors. The feature analysis and crowd identification of social media rumor refuters based on machine learning methods can shed light on the rumor refutation process. This paper analyzed the association between user features and rumor refuting behavior in five main rumor categories: economics, society, disaster, politics, and military. Natural language processing (NLP) techniques are applied to quantify the user’s sentiment tendency and recent interests. Then, those results were combined with other personalized features to train an XGBoost classification model, and potential refuters can be identified. Information from 58,807 Sina Weibo users (including their 646,877 microblogs) for the five anti-rumor microblog categories was collected for model training and feature analysis. The results revealed that there were significant differences between rumor stiflers and refuters, as well as between refuters for different categories. Refuters tended to be more active on social media and a large proportion of them gathered in more developed regions. Tweeting history was a vital reference as well, and refuters showed higher interest in topics related with the rumor refuting message. Meanwhile, features such as gender, age, user labels and sentiment tendency also varied between refuters considering categories.