Abstract
The existing methods have been used the Zenith Total Delay (ZTD) or Precipitable Water Vapor (PWV) derived from Global Navigation Satellite System (GNSS) for rainfall forecasting. However, the occurrence of rainfall is highly related to a myriad of atmospheric parameters, and a good forecast result cannot be obtained if it only depends on a single predictor. This study focused on rainfall forecasting by using a number of atmospheric parameters (such as: temperature, relative humidity, dew temperature, pressure, and PWV) based on the improved Back Propagation Neural Network (BP–NN) algorithm. Results of correlation analysis showed that each meteorological parameter contributed to rainfall. Therefore, a short-term rainfall forecast model was proposed based on an improved BP–NN algorithm by using multiple meteorological parameters. Two GNSS stations and collocated weather stations in Singapore were used to validate the proposed rainfall forecast model by using three years of data (2010–2012). True forecast (TFR), false forecast (FFR), and missed forecast (MFR) rate were introduced as evaluation indices. The experimental result revealed that the proposed model exhibited good performance with TFR larger than 96% and FFR of approximately 40%. The proposed method improved TFR by approximately 10%, whereas FFR was comparable to existing literature. This forecasted result further verified the reliability and practicability of the proposed rainfall forecasting method by using the improved BP–NN algorithm.
Highlights
Water vapor is the most important and abundant greenhouse gas in the troposphere and plays an important role in atmospheric radiation, energy balance, and hydrological cycle[1,2]
Back propagation (BP)–NN is a kind of multilayer feed forward artificial neural network with mono directional transmissions[22,23], which has the advantages of memory association, solving complex internal mechanism problems, independent learning and adaptive ability, and parallel processing of data[24]
In addition[32], proposed a multilayer feedforward neural network model for weighted mean temperature of atmospheric water vapor predicting, and the result shows the good performance of NN model on global scale33. proposed a new zenith total delay (ZTD) model based on a back propagation neural network, and the ZTD prediction accuracy has been improved by more than 12.4%
Summary
Overcome the disadvantages of slow convergence speed, local minimum, and training paralysis of the traditional BP neural network. The TFR of the simulated result of the three schemes is larger than 98%, whereas the FFR ranged from 17–47% in the two stations This table indicates that the TFR of Schemes 2 and 3 was comparable, whereas the FFR decreased when more training data were used to establish the rainfall forecast model based on the improved BP–NN algorithm. The average values of TFR, FFR, and MFR of the three schemes in the two stations were 99.18%, 33.90%, and 0.82, respectively These results validated the feasibility of the proposed rainfall forecast model based on the improved BP–NN algorithm. The TFR and FFR of the proposed rainfall forecast model with the improved BP–NN algorithm could reach up to 92% to 99% and 35% to 43%, respectively We show that the average TFR and of the three schemes are above 96% and approximately 40%, respectively. This result indicated that the proposed rainfall forecast model should be trained by using as much data as possible
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