With various developments, the concept of the smart city has attracted great attention all over the world. To many, it is a good intelligent response to the needs of people’s livelihoods, environmental protection, public safety, etc. A weather-smart grid is an important component of the smart city, and the health of the weather-smart grid will directly affect the health of the smart city. Efficient and accurate predictions about air quality levels can provide a reliable basis for societal decisions, safety for smart transportation, and weather-related disaster prevention and preparation. To improve the time performance and accuracy of prediction with a large amount of data, this paper proposes an improved decision tree method. Based on an existing method, the model is improved in two aspects: the feature attribute value and the weighting of the information gain. Both accuracy and computational complexity are improved. The experimental results demonstrate that the improved model has great advantages in terms of the accuracy and computational complexity compared with the traditional methods. Additionally, it is more efficient in addressing classification and prediction with a large amount of air quality data. Moreover, it has good prediction ability for future data.

With the development of information technology, it is possible to explore the spatial-temporal distribution characteristics of taxi travel demand by examining taxi GPS location data in order to master the actual supply and demand levels of different hot spots at different time periods. At present, in hot spot mining, the existing research on the clustering of passenger hot spots has some performance problems, such as insufficient clustering accuracy and high algorithm time complexity. The purpose of this paper is to propose a two-level subdivision concept and improve the K-means++ algorithm to finish the fine clustering of hot spots of taxi passengers. The first-level subdivision establishes a dynamic adjustable region with time and geographical range. In the second layer, a Gaussian mixture model is used for the data distribution statistics, and the optimal subdivision area number is determined according to the minimum principle of the Akaike information criterion and Bayesian information criterion. The SSE (sum of the square distance errors) is used to determine the optimal cluster number k for each local area. Finally, the K-means++ algorithm is used to complete the clustering of each local area. A week of green taxi data from New York City was used to validate the method and compare it to the traditional K-means and DBSCAN approaches. The proposed method achieved better accuracy with comparable time consumption. This demonstrated the value of the approach for hot spot data mining although clustering still has some important advantages. In addition, the hot spots in the morning peak and weekend are displayed visually, which is helpful to provide the guidance for urban transportation and planning.

Heart disease is a serious threat to human health. Accurate prediction is very important for disease prevention and treatment. The purpose of this study is to establish a more suitable prediction model of heart disease. Based on LightGBM, we have deeply integrated bootstrap sampling and weighting technology. We repeatedly use multiple parameters of LightGBM to perform bootstrap sampling on the original training set. Through this process, we not only obtain training sub-models for various training subsets but also mine rich data features. In the process of cross-validation, the weight coefficient for each sub-model is carefully determined by comprehensively evaluating multiple key performance indicators, including accuracy, precision, recall, and the F1 score. This approach effectively highlights the role of high-quality sub-models. In the test stage, each sub-model is weighted according to the weight corresponding to its specific parameter combination, and finally, an accurate prediction result is obtained. Compared with the traditional prediction model, the model shows better comprehensive performance in terms of various performance metrics such as accuracy, precision, recall, and F1 score, and also performs better in the paired t-test. Moreover, compared with the baseline model, the phenomenon of overfitting is obviously reduced. Although the model has not been verified by external data sets, it has, to a certain degree, significantly boosted its predictive ability, universality, and stability. Moreover, it has provided a feasible scheme for heart disease prediction, which is expected to play a crucial role in clinical auxiliary diagnosis and disease management. The research shows that this model has obvious advantages in heart disease prediction and can effectively enhance the accuracy and reliability of prediction.

The fresh water in rivers beneath the Earth’s surface is as significant to humans as that on the surface. However, the water quality is difficult to monitor due to its unapproachable nature. In this work, we consider building networks to monitor water quality in subterranean rivers. The network node is designed to have limited functions of floating and staying in these rivers when necessary. We provide the necessary conditions to set up such networks and a topology building method, as well as the communication process between nodes. Furthermore, we provide every an node’s energy consumption model in the network building stage, the data acquiring and transmission stage. The numerical results show that the energy consumption in every node is different, and the node number should be moderate to ensure energy efficiency.