Nowadays, cities meet numerous sustainable development challenges in facing growing urban populations and expanding urban areas. The monitoring and simulation of land use and land-cover change have become essential tools for understanding and managing urbanization. This paper interprets and predicts the expansion of seven different land use types in the study area, using the PLUS model, which combines the Land use Expansion Analysis Strategy (LEAS) and the CA model, based on the multi-class random patch seed (CARS) model. By choosing a variety of driving factors, the PLUS model simulates urban expansion in the metropolitan area of Hangzhou. The accuracy of the simulation, manifested as the kappa coefficient of urban land, increased to more than 84%, and the kappa coefficient of other land use types was more than 90%. To a certain extent, the PLUS model used in this study solves the CA model’s deficiencies in conversion rule mining strategy and landscape dynamic change simulation strategy. The results show that various types of land use changes obtained using this method have a high degree of accuracy and can be used to simulate urban expansion, especially over short periods.

Context: At present, many cities are facing severe water-resources problems caused by urbanisation. With the development of remote sensing and geostatistics, they have been widely used in urban water-resource monitoring. Aims: To review and summarise the application of remote sensing and geostatistics in monitoring urban water resources and prospect for their furtherdevelopment. Methods: First, bibliometrics was used to analyse the existing literature in this field. We then discuss the use of remote sensing and geostatistics to improve urban water-resources monitoring capacity, focusing on the classification of technologies and equipment and their applications in urban surface-water and urban groundwater monitoring. Finally, a look at the future research direction is taken. Conclusions: In the past decade, the relevant research has shown an upward trend. The use of remote sensing and geostatistics can improve the city’s water-resource monitoring capacity, thereby promoting better use of water resources in cities. Implications: In the future, with the development and addition of deep learning, remote-sensing and geographic-analysis systems can be used to conduct remote-sensing monitoring and data analysis on urban water resources more accurately, intelligently, and quickly, and improve the status of urban water resources.

In the era of big data, the Internet is enmeshed in people’s lives and brings conveniences to their production and lives. The analysis of user preferences and behavioral predictions of user data can provide references for optimizing information structure and improving service accuracy. According to the present research, user’s behavior on social networking sites has a great correlation with their personality, and the five characteristics of the OCEAN (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) personality model can cover all aspects of a user’s personality. It is important in identifying a user’s OCEAN personality model to analyze their digital footprints left on social networking sites and to extract the rules of users’ behavior, and then to make predictions about user behavior. In this paper, the Latent Dirichlet Allocation (LDA) topic model is first used to extract the user’s text features. Second, the extracted features are used as sample input for a BP neural network. The results of the user’s OCEAN personality model obtained by a questionnaire are used as sample output for a BP neural network. Finally, the neural network is trained. A mapping model between the probability of the user’s text topic and their OCEAN personality model is established to predict the latter. The results show that the present approach improves the efficiency and accuracy of such a prediction.

In recent years, haze pollution is frequent, which seriously affects daily life and production process. The main factors to measure the degree of smoke pollution are the concentrations of PM2.5 and PM10. Therefore, it is of great significance to study the prediction of PM2.5/PM10 concentration. Since PM2.5 and PM10 concentration data are time series, their time characteristics should be considered in their prediction. However, the traditional neural network is limited by its own structure and has some weakness in processing time related data. Recurrent neural network is a kind of network specially used for sequence data modeling, that is, the current output of the sequence is correlated with the historical output. In this paper, a haze prediction model is established based on a deep recurrent neural network. We obtained air pollution data in Chengdu from the China Air Quality Online Monitoring and Analysis Platform, and conducted experiments based on these data. The results show that the new method can predict smog more effectively and accurately, and can be used for social and economic purposes.

Context: Numerous dams have been built in China to develop hydropower, a sustainable and clean energy source. In recent years, the impact of dam construction on the regional climate has gradually attracted the attention of researchers. Aims: This study has evaluated the impact of large-scale dam construction on regional precipitation. Methods: This paper used the precipitation data of more than 2400 national stations of the China National Meteorological Information Center from 1990 to 2012. The regional precipitation data before and after the construction of the Xiaolangdi Dam and the Three Gorges Dam on the Yangtze River were analysed using geostatistical tools. Wavelet transform and Yamamoto signal-to-noise ratio analysis were further adopted. Key results: Analysis of the variation points of precipitation characteristics confirmed the correlation between dam construction and regional precipitation; the precipitation values in the two dam study areas had an increasing trend after the completion of the dams, and the fitting trend line showed an obvious increasing trend. Conclusions: According to the analysis of precipitation variation points, it can be concluded that the establishment of the dam affected the precipitation in the area of ~200 km upstream and increased the precipitation value in this area. The study showed that dam construction has a strong correlation with regional precipitation. Implications: It is speculated that the construction of super dams will have a greater impact on precipitation.