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Water option trading aims at solving the problem of uneven spatial and temporal distribution of water resources. With the change of global climate, rainfall has become an important factor leading to the uneven spatial and temporal distribution of water resources. In this paper, Markov chain is used to divide and forecast the rainfall scenarios. On the basis of rainfall forecast, the related costs of water option trading are determined. The forecast results of rainfall index are integrated into the determination of the ultimate overprice of water options. The improved Black- Scholes option pricing model is used to determine the rights of water options trading based on the forecast results of rainfall. Finally, the rationality of the calculation results is verified by using the extended linear expenditure model, which proves the rationality of the transaction price determined by the model. Taking Guangdong province as an example, this paper verifies the necessity of incorporating rainfall index into water option trading and the rationality of the related costs of water option trading determined by the model, and proves the superiority of fluctuating water price over stepped water price. Water option trading considering rainfall fully reflects the relationship between supply and demand in water market, and improves the scientificity of water option trading pricing in China. [ABSTRACT FROM AUTHOR]

Predicting the future activity location and trajectory of residents can provide essential information for smart urban management such as epidemic control, traffic facilitation, public security, etc. However, the current personal location prediction methods generally focus on the mining of individual’s historical travel patterns, and seldom consider the feature of different travel stay points. This paper aims to propose a location prediction model utilizing stay point feature extraction. The model firstly constructs the historical trajectory links based on trajectory data, performs the location discovery rules to transform historical trajec⁃ tory links into stay point links and clusters the stay points to form clustering links. Secondly, the model ex⁃ tracts the feature information (entry time, departure time, weather and land use) from different stay points and extracts the space feature from clustering links. Finally, the cluster links with feature information is in⁃ troduced into long short ⁃ term memory (LSTM) network for customization to implement the personal loca⁃ tion prediction capability. By using a 23⁃day trajectory location data of more than 3 million volunteer users in Shenzhen city, China. The results show that the location prediction F⁃score of our proposed model is bet⁃ ter than variable order Markov model (about 5.5% performance gains) and the traditional N⁃order Markov model (about 7% performance gains). The model also introduces approximately 6.6% performance gains by utilizing the temporal and weather features of stay points. The model shows the capability of utilizing travel stay point feature for personal next location prediction. [ABSTRACT FROM AUTHOR]