Extracting evolving communities in social networks has attracted much attention recently due to its usefulness in the area of social media. Most existing models assume that network structures evolve monotonically and fail to leverage the fluctuating variation in the real world. Moreover, it is difficult to extract valuable community knowledge from previous snapshots, leading to a negative transfer to the current snapshot. In this article, we design a novel transferring strategy for dynamic community detection based on dual-population cooperation and competition. The transfer strategy guides the search by leveraging the meaningful community structures among previous snapshots based on the similarity between the current and all previous ones. Furthermore, to avoid the problem of insufficient population diversity caused by previous single-population algorithms, this article utilizes dual-population cooperative competition for multiobjective optimization. An intercooperation method effectively interchanges information according to normalized mutual information of different individuals in dual-population. Each population optimizes according to different objectives with a role-oriented teaching–learning-based optimizer to compensate for defects such as many hyperparameters, declining diversity, and insufficient convergence. Top students integrate the fine-grained strategy to mutate boundary nodes depending on embedding-based node activity; Ordinary students fuse the coarse-grained method to separate loosely connected subcommunities, while the bottom students do normal learning. Experimental results indicate that our approach outperforms state-of-the-art methods with high consistency.

Meteorological operations rely heavily on quantitative precipitation estimation (QPE) derived from weather radars, calibrated against ground-based rain gauges. However, the limitations of rain gauge networks, namely their low spatial representativeness and often sparse and inhomogeneous distribution, pose challenges. Against this backdrop, commercial microwave links (CMLs), which opportunistically offer denser rainfall observations, emerge as viable alternatives for fusion with radar data, with particular potential in economically underdeveloped developing countries. This article introduces an adaptive data fusion method that dynamically calibrates the relationship between radar reflectivity factor (Z) and rain rate (R) to adapt the variations in weather systems, leveraging attenuation data from CMLs and radar reflectivity factor data. The experimental study was conducted based on data from 47 CMLs and one S-band weather radar in Jiangyin City, alongside eight rain gauges used for validation purposes. The results of the proposed method agree well with the reference measurements and reach a Pearson’s correlation coefficient of 0.65. Furthermore, it improves the coefficient of variation and root-mean-square error by 17% (24%) and 15% (27%), respectively, over the results from the empirical Z–R relationship (CMLs-based radar mean-field bias adjustment method). This study not only validates the method’s efficacy in delivering precise quantitative precipitation estimates but also delves into the influence of weather system instability and observation time on its performance.

The significance of rainfall detection is generally acknowledged, and the linked opportunistic approach to giving it new momentum has also been extensively demonstrated. This study conducted a rainfall monitoring experiment using two stationary smartphones in a light indoor setting, receiving downlink signals from a long-term evolution (LTE) base station for two months. The analysis reveals that the received signals are not stable during dry periods, but the reference signal receiving power (RSRP) and reference signal strength indicator (RSSI) parameters can produce a certain degree of degradation during rainfall. To address the concern about frequent switching of connection to the base station resulting in different fluctuation levels, the standardized standard deviations of the four signaling parameters for different time windows were extracted as features to build a dry-rainy classification model. Furthermore, a rain rate class identification model is also established based on the extraction of the specific rain-induced attenuation and standardized standard deviation. The experiment mentioned has shown promising results in rainfall monitoring based on widely available opportunistic signal sources from wireless terminals.

In this paper, a novel adaptive rain-induced attenuation-rain rate (A-R) relationship model is proposed, which is built based on a clustering algorithm to improve the accuracy of rainfall inversion based on commercial microwave link (CML). The performance of this adaptiveA-Rrelationship is systematically evaluated at eight common CML operating frequencies with five years of raindrop size distribution data from a PARSIVEL disdrometer. The results show that the overall single-frequency adaptiveA-Rrelationship has a better performance than the ITU-R and local fittingA-Rrelationships (lower root mean square error, relative bias (an improvement of around 10% at several of the frequencies), and significant unbiasedness). In addition, the effect of cluster number on theA-Rrelationship and the performance of dual-frequency or dual-polarization adaptiveA-Rrelationships are also discussed. Finally, the relative errors of theseA-Rrelationships on the inversion of cumulative rainfall are depicted to visualize the performance, which demonstrate that certain single/dual-frequency adaptiveA-Rmodels have good performance in most cases.

Recently, commercial microwave links (CMLs) have become one of the alternative sources of precipitation information. In this technique, a core issue is the separation of data into dry and wet periods. However, most of the existing classification methods require rainfall data for calibration. Furthermore, they face the challenge of unbalanced datasets due to the unevenness of the spatiotemporal distribution of rainfall. To solve these problems, we propose a one-class classification machine learning method based on autoencoders (AE), which can achieve the classification of dry and wet periods only using dry period data without statistical modeling of the rainfall intensity distribution. Based on the new method, four CMLs from Jiangyin were used to carry out classification and rainfall inversion experiments. The rolling standard deviation (RSD) method was used for comparison. The results show that the AE method has slightly better classification performance compared to the calibrated RSD method.

Microwave links as a novel method of rainfall monitoring have been extensively studied over the last two decades. Still, the applicability of$\gamma $(rain-induced attenuation rate)–$R$(rain rate) relationship in quantitative rainfall inversion with microwave link under tilted conditions has not been systematically investigated to date. With the establishment of rain-induced attenuation fields based on multiple sets of measured raindrop spectral profile data from micro-rain radar, microwave links under various tilted conditions are simulated and the performance of$\gamma - R$relationship in inversion of rain rate is theoretically analyzed. Afterward, the effects of link height, tilt angle, and rainfall reference point variations on the rainfall inversion performance are discussed in depth from the theoretical and data analysis perspectives. Furthermore, the connection between the study and the satellite-ground link-based rainfall measurement technique is mapped. Finally, the applicability analysis to different regions and the introduction of errors is also investigated.

Rainfall inversion based on commercial microwave links (CMLs) has been extensively studied and experimented. However, as necessary part of inversion, the error caused by theA - Rrelationship is lack of systematic evaluation. Based on the measured raindrop size distribution (RSD) data recorded by a disdrometer, the theoretical rain-induced attenuation and rainrate are calculated, and on this basis, the error between the inversed rainrate based onA - Rrelationship from ITU-R recommendation and the real rainrate is compared under different rainfall types, rainfall classes and spatially heterogeneous rain cell. Firstly, the errors ofA - Rrelationship under different rainfall classes show thatA - Rrelationship can effectively invert rainrate between 18 and 26 GHz for all classes, while the error increases significantly outside this frequency range, especially for heavy rainfall. In addition, theA - Rrelationship performance of different rainfall types is evaluated. There is a significant difference in the unbiasedness of convective rainfall and stratiform rainfall, which are positive and negative bias at high frequency (greater than 50 GHz), respectively. Furthermore, aiming at the inhomogeneity of rainfall spatial distribution, the rainrate distribution and the corresponding path rain-induced attenuation are simulated based on an exponential model, and the influence of spatial location difference on the accuracy ofA - Rrelationship is deeply analyzed. Finally, the error source and influence of localizedA - Rrelationship, integration time and link length are discussed.

This article proposes a new rainfall-type classification method using the differential attenuation rates (DARs) calculated from microwave links with multiple frequencies and dual polarization. Several machine-learning algorithms were employed for classification [decision tree (DT), probabilistic neural network (PNN), Gaussian discriminant analysis (GDA), and logistic regression (LR)]. This method was simulated by two or three frequencies (combination of frequencies of 15, 25, 38, 60, and 80 GHz). The results showed that DT, PNN, GDA, and LR achieved maximum accuracies of 88.9%, 84.6%, 66.8%, and 67.6% for the dual-frequency model, respectively, and 89.6%, 85.8%, 67.6%, and 68.7% for the tri-frequency model, respectively, which is obviously better than the classification method based on the sequence of rain rates from the ITU-R model. The model accuracies based on GDA and LR algorithms are obviously lower than those based on DT and PNN due to the defects of the algorithms. In addition, the effects of different noise levels on classification performance are also discussed.