To ensure the security of data transmission and recording in Internet environment monitoring systems, this paper proposes a study of a secure method of blockchain data transfer based on homomorphic encryption. Blockchain data transmission is realized through homomorphic encryption. Homomorphic encryption can not only encrypt the original data, but also ensure that the data result after decrypting the data is the same as the original data. The asymmetric encrypted public key is collected by Internet of things (IoT) equipment to realize the design of blockchain data secure transmission method based on homomorphic encryption. The experimental results show that the accuracy of the first transmission is as high as 88% when using the transmission method in this paper. After several experiments, the transmission accuracy is high by using the design method in this paper. In the last test, the transmission accuracy is still 88%, and the data transmission effect is relatively stable. At the same time, compared to the management method used in this article, the transfer method used in this paper is more reliable than the original transfer method and is not prone to data distortion. It can be seen that this method has high transmission accuracy and short transmission time, which effectively avoids the data tampering caused by too long time in the transmission process.

Flooding is becoming a prominent issue in coastal cities, flood forecasting is the key to solving this problem. However, the lack and imbalance of research data and the insufficient performance of the model have led to the complexity and uncontrollability of flood forecasting. To forecast coastal floods accurately and reliably, the Internet of Things technology is used to collect data on floods and flood factors in smart cities. An ensemble learning method based on Bayesian model combination (BMC-EL) is designed to predict flood depth. First, flood intensity classification and K-fold cross-validation are introduced to generate multiple training subsets from the training set to realize uniform sampling and increase the diversity of subsets. Second, the backpropagation neural network (BPNN) and random forest (RF) are used as the base learners to build the prediction model and then import it into training subsets for training purposes. Finally, based on the prediction performance of the base learner in the validation sets, the Bayesian model combination strategy is formulated to integrate and output predicted values. We describe experiments conducted to forecast flood depth 1 h in advance that several machine learning models were trained and tested using real flood data taken from Macao, China. The models include linear regression, support vector machine, BPNN, RF and BMC-EL models. Results prove the accuracy and reliability of the BMC-EL method in flood forecasting for coastal cities.

Using data-driven models to predict floods in advance is one of the current effective methods and hot researches to reduce urban flood disasters. In order to improve the prediction accuracy, it is necessary to select the appropriate flood hazard factors and the number of training samples to construct the prediction model. In our current research, an artificial neural network (i.e., the back-propagation neural network, BPNN) model was developed to predict the flood depth in the next hour. A case study of the urban flood during six typhoons in Macau of China was conducted to prove the performance of the proposed model. The flood depth was collected as output; after analyzing their correlation to the flood typhoon optimum track, urban weather, tides, geographic height and water depth increment of the submerged area were used as input. As a result, four models trained with different sample numbers were developed for training and testing. The model performances were examined using average absolute error, root mean square error and the coefficient of determination. The results show that in this case study, the 30-min scale model provides reliable predictions and can provide useful decision support for the prevention and mitigation of flood disasters in coastal urban.

Natural disasters are hazardous geophysical, meteorological, hydrological, climatological, and/or biological events that disturb human and natural environments, causing injuries, casualties, property damages, and business interruptions. Sound analysis is required regarding the effective hazard preparedness for, response to, mitigation of, and recovery from natural disasters. This research proposes an expected risk analysis model of world natural disasters recorded for 1900–2015 in the Emergency Disaster Database compiled by the Centre for Research on the Epidemiology of Disaster. The model produces consistent estimates of country-level risks in terms of human casualty and economic loss. The expected risks, along with their standard deviations, and ranks for world 208 countries, are analyzed with highlights for the top 10, 20 and 30 countries. Normalized expected risks by country population density and per capita gross domestic product (GDP) are also analyzed to further understand the relationships between risks and socio-economic measures. The results show that the model is a reasonably effective alternative to the existing risk analysis methods, based on the high correlations between the observed and estimated total risks. While riskier countries with higher expected risks and standard deviations are found in all continents, some developing countries such as China, India, Bangladesh, and Brazil, or developed countries, such as the United States, Japan, and Germany, are the hot-spots of global natural disasters. The model can be used as a new alternative approach to conduct country-level risk assessments or risk analyses of fatality, injured, affected, and damage—especially for countries’ governments to make sound disaster preparation, and mitigation decisions, sustainable policies, or plans regarding natural disasters.

With the rapid development of urban road traffic and the increasing number of vehicles, how to alleviate traffic congestion is one of the hot issues that need to be urgently addressed in building smart cities. Therefore, in this paper, a nonlinear multi-objective optimization model of urban intersection signal timing based on a Genetic Algorithm was constructed. Specifically, a typical urban intersection was selected as the research object, and drivers’ acceleration habits were taken into account. What’s more, the shortest average delay time, the least average number of stops, and the maximum capacity of the intersection were regarded as the optimization objectives. The optimization results show that compared with the Webster method when the vehicle speed is 60 km/h and the acceleration is 2.5 m/s², the signal intersection timing scheme based on the proposed Genetic Algorithm multi-objective optimization reduces the intersection signal cycle time by 14.6%, the average vehicle delay time by 12.9%, the capacity by 16.2%, and the average number of vehicles stop by 0.4%. To verify the simulation results, the authors imported the optimized timing scheme into the constructed Simulation of the Urban Mobility model. The experimental results show that the authors optimized timing scheme is superior to Webster’s in terms of vehicle average loss time reduction, carbon monoxide emission, particulate matter emission, and vehicle fuel consumption. The research in this paper provides a basis for Genetic algorithms in traffic signal control.