Short term forecasting for HFSWR sea surface current mapping using artificial neural network

Taiwan Ocean Research Institute (TORI) established the Taiwan Ocean Radar Observing System (TOROS) based on the CODAR high frequency surface wave radar (HFSWR). The TOROS is the first network having complete, contiguous HFSWR coverage of nation’s coastline in the world. This network consisting of 17 SeaSonde radars offers coverage across approximately 190,000 square kilometers an area, over five times the size of Taiwan’s entire land mass. In the southernmost and narrowest part of Taiwan, two 13 MHz and one 24 MHz radars were established along the NanWan Bay since June, 2014.

NanWan Bay, the southern tip of Taiwan, is a southward semi-enclosed basin bounded by two capes and is open to the Luzon Strait. The distance between the two caps is around 12 km, and the distance from the northernmost point of the bay to the caps are 5 and 11 km, respectively. Strong tidal currents dominate the ocean circulation in the NanWan Bay and induce obvious upwelling of cold water that intrudes on to the shallow regions of NanWan Bay around spring tides. From late fall to early spring, the seaward wind dominated by the northeast monsoon often destratifies the water column and decreases the sea surface temperature inside the Bay (Lee et al, 1997). Furthermore, the Nanwan Bay is famous with well-developed fringing reefs distributed along the shoreline. In this area, 230 species of scleractinian corals, nine species of non-scleractinian reef-building corals, and 40 species of alcyonacean corals have been recorded (Dai, 1991). NanWan, in the shape of a beautiful arch, attracts large crowds of people to take all kinds of beach or water activities every summer.

In order to improve the applicability of HFSWR ocean surface current data on search and rescue issue and evaluation of coral spawn dispersal, a short term forecasting model using artificial neural network (ANN) was developed in this study. That ocean surface current vectors obtained from tidal theory are added as inputs in artificial neural network model is found to improve prediction ability for current vectors. The optimum structure of the present ANN model for each ocean current grid is set up from examining the learning rate, moment factor, input parameters, numbers of hidden layer, learning times and input length. Results show that the ANN model have better accuracy of short-term forecasting.