When pure linear neural network (PLNN) is used to predict tropical cyclone tracks (TCTs) in South China Sea, whether the data is normalized or not greatly affects the training process. In this paper, min.-max. method and normal distribution method, instead of standard normal distribution, are applied to TCT data before modeling. We propose the experimental schemes in which, with min.-max. method, the min.-max. value pair of each variable is mapped to (−1, 1) and (0, 1); with normal distribution method, each variable’s mean and standard deviation pair is set to (0, 1) and (100, 1). We present the following results: (1) data scaled to the similar intervals have similar effects, no matter the use of min.-max. or normal distribution method; (2) mapping data to around 0 gains much faster training speed than mapping them to the intervals far away from 0 or using unnormalized raw data, although all of them can approach the same lower level after certain steps from their training error curves. This could be useful to decide data normalization method when PLNN is used individually.

The rapidly intensifying typhoon Mirinae (1603) originated in the South China Sea (SCS) has been selected to analyze the high and low level circulations, vertical wind shear (VWS) and ocean conditions in and around the SCS when Mirinae was strengthening. Further, the fuzzy neural network (FNN) ensemble model has been developed for 24-hour intensity prediction based on climatology and persistence factors and dynamic factors of numerical prediction products as potential predictors. The underlying conditions were favorable for Mirinae, however, it did not be intensified to a strong typhoon because of the weak high and low level circulations and strong VWS. For the FNN ensemble prediction model input, predictors were selected by employing both a novel stepwise variable selection algorithm based on the k-nearest mutual information estimation and Laplacian Eigenmaps of manifold learning, and then the main prediction information hidden in high dimensional data set was extracted with low-dimensional structure. Results show that, the predictions of the new model are stable and reliable, and the forecast accuracy is high, providing a new forecasting tool and modeling method for the objective forecast of typhoon intensity.

The goal of crowd counting is to estimate the number of people in the image. Presently, use regression to count people number became a mainstream method. It is worth noting that, with the development of convolutional neural networks (CNN), methods that are based on CNN have become a research hotspot. It is a more interesting topic that how to locate the site of the person in the image than simply predicting the number of people in the image. The perspective transformation present is still a challenge, because perspective distortion will cause differences in the size of the crowd in the image. To devote perspective distortion and locate the site of the person more accuracy, we design a novel framework named Adaptive Learning Network (CAL). We use the VGG as the backbone. After each pooling layer is output, we collect the 1/2, 1/4, 1/8, and 1/16 features of the original image and combine them with the weights learned by an adaptive learning branch. The object of our adaptive learning branch is each image in the datasets. By combining the output features of different sizes of each image, the challenge of drastic changes in the size of the image crowd due to perspective transformation is reduced. We conducted experiments on four population counting data sets (i.e., ShanghaiTech Part A, ShanghaiTech Part B, UCF_CC_50 and UCF-QNRF), and the results show that our model has a good performance.

The paper proposes a method for correcting the precipitation forecast of the European Centre for Medium-Range Weather Forecasts (ECMWF) model. The method first uses the ECMWF physical quantity field to draw the flow field diagram in the form of weather, and uses it as the input of the convolutional neural network(CNN) and performs feature extraction on it. Secondly, select several characteristic factors that are highly correlated with the forecast object from the many characteristic factors. Finally, the selected characteristic factors and the precipitation forecast factors of the ECMWF model are used as the input factors of the random forest regression model for modeling and forecasting. Through the correction forecast test of the next 24h precipitation at 10 forecast test stations, the results show that the ECMWF precipitation correction forecast method proposed in this paper is 15% less than the forecast method using ECMWF interpolation to the station on the MAE and RMSE indicators. And 17%. At the same time, on the TS scores of rainstorms above 24h, the revised forecast method proposed in this paper has a false alarm rate significantly lower than the forecast method of ECMWF interpolation to the station.