A PNN prediction scheme for local tropical cyclone intensity over the South China Sea

Using eXtreme Gradient BOOSTing to Predict Changes in Tropical Cyclone Intensity over the Western North Pacific

“…The MAEs of the forecasts with 6, 12, 18, and 24 h lead times for the test samples used were 1.61, 2.44, 3.10, and 3.70 m/s, respectively, for the XGBOOST model. The results indicate that the XGBOOST model developed in this study can be used to improve TC intensity forecast accuracy and can be considered a better alternative to conventional operational forecast models for TC intensity prediction.The characteristics that affect TC intensity are nonlinear and thus difficult to predict [2]. In recent years, many researchers have studied TC intensity prediction primarily using numerical forecasting and statistical methods [6][7][8][9][10].…”

“…One of the reasons for this is that the internal structure of storms is not yet sufficiently understood. TCs have an asymmetric structure, which may be caused by thermal and dynamic factors such as uneven distribution of sea surface temperature and humidity, horizontal or vertical shear, and asymmetric distribution of convection [2,17]. Changes in TC intensity are controlled by many environmental and oceanic variables, including oceanic heat, vertical wind shears, and underlying surfaces changes [9].…”

“…Changes in TC intensity are controlled by many environmental and oceanic variables, including oceanic heat, vertical wind shears, and underlying surfaces changes [9]. Because these factors exhibit nonlinear characteristics, TC intensity is difficult to predict accurately using traditional statistical and numerical forecasting methods [2]. Thus, conventional linear statistical methods have difficulties when applied to nonlinear systems (e.g., forecasting TC intensity).…”

“…At present, artificial neural network (ANN) methods can be used to effectively solve complex nonlinear issues, and numerous studies have concentrated on novel modeling techniques to improve the accuracy of TC intensity predictions [18][19][20].In recent years, studies have clearly showed the potential of machine learning systems such as back propagation neural networks (BPNNs) to forecast TC intensity [18]. However, the traditional BPNN is unstable and less accurate than a probabilistic neural network (PNN) [2], which is a radial basis function neural (RBFN) network that is strongly fault-tolerant, and has adaptive capabilities. The k-nearest-neighbor (K-NN) machine learning algorithm has also been applied to predict TC intensity based on microwave imager data [21].…”

“…The characteristics that affect TC intensity are nonlinear and thus difficult to predict [2]. In recent years, many researchers have studied TC intensity prediction primarily using numerical forecasting and statistical methods [6][7][8][9][10].…”