scholarly journals A Comparison of BPNN, GMDH, and ARIMA for Monthly Rainfall Forecasting Based on Wavelet Packet Decomposition

Water ◽  
2021 ◽  
Vol 13 (20) ◽  
pp. 2871
Author(s):  
Wenchuan Wang ◽  
Yujin Du ◽  
Kwokwing Chau ◽  
Haitao Chen ◽  
Changjun Liu ◽  
...  
Keyword(s):  
Wavelet Packet ◽  
Arima Model ◽  
Back Propagation ◽  
Forecast Model ◽  
Monthly Rainfall ◽  
Ensemble Prediction ◽  
Group Method ◽  
Wavelet Packet Decomposition ◽  
Rainfall Forecasting ◽  
Bpnn Model

Accurate rainfall forecasting in watersheds is of indispensable importance for predicting streamflow and flash floods. This paper investigates the accuracy of several forecasting technologies based on Wavelet Packet Decomposition (WPD) in monthly rainfall forecasting. First, WPD decomposes the observed monthly rainfall data into several subcomponents. Then, three data-based models, namely Back-propagation Neural Network (BPNN) model, group method of data handing (GMDH) model, and autoregressive integrated moving average (ARIMA) model, are utilized to complete the prediction of the decomposed monthly rainfall series, respectively. Finally, the ensemble prediction result of the model is formulated by summing the outputs of all submodules. Meanwhile, these six models are employed for benchmark comparison to study the prediction performance of these conjunction methods, which are BPNN, WPD-BPNN, GMDH, WPD-GMDH, ARIMA, and WPD-ARIMA models. The paper takes monthly data from Luoning and Zuoyu stations in Luoyang city of China as the case study. The performance of these conjunction methods is tested by four quantitative indexes. Results show that WPD can efficiently improve the forecasting accuracy and the proposed WPD-BPNN model can achieve better prediction results. It is concluded that the hybrid forecast model is a very efficient tool to improve the accuracy of mid- and long-term rainfall forecasting.

scholarly journals Examining the Applicability of Wavelet Packet Decomposition on Different Forecasting Models in Annual Rainfall Prediction

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 1997
Author(s):  
Hua Wang ◽  
Wenchuan Wang ◽  
Yujin Du ◽  
Dongmei Xu
Keyword(s):  
Wavelet Packet ◽  
Moving Average ◽  
Back Propagation ◽  
Processing Technique ◽  
Annual Rainfall ◽  
Wavelet Packet Decomposition ◽  
Rainfall Forecasting ◽  
Promising Alternative ◽  
Forecasting Models ◽  
Precipitation Prediction

Accurate precipitation prediction can help plan for different water resources management demands and provide an extension of lead-time for the tactical and strategic planning of courses of action. This paper examines the applicability of several forecasting models based on wavelet packet decomposition (WPD) in annual rainfall forecasting, and a novel hybrid precipitation prediction framework (WPD-ELM) is proposed coupling extreme learning machine (ELM) and WPD. The works of this paper can be described as follows: (a) WPD is used to decompose the original precipitation data into several sub-layers; (b) ELM model, autoregressive integrated moving average model (ARIMA), and back-propagation neural network (BPNN) are employed to realize the forecasting computation for the decomposed series; (c) the results are integrated to attain the final prediction. Four evaluation indexes (RMSE, MAE, R, and NSEC) are adopted to assess the performance of the models. The results indicate that the WPD-ELM model outperforms other models used in this paper and WPD can significantly enhance the performance of forecasting models. In conclusion, WPD-ELM can be a promising alternative for annual precipitation forecasting and WPD is an effective data pre-processing technique in producing convincing forecasting models.

scholarly journals Improving annual precipitation prediction using extreme learning machine with wavelet packet decomposition

2021 ◽  
Author(s):  
Hua Wang ◽  
Wenchuan Wang ◽  
Yu-jin Du ◽  
Dong-mei Xu ◽  
Yi-duo Zhang
Keyword(s):  
Extreme Learning Machine ◽  
Wavelet Packet ◽  
Moving Average ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Annual Precipitation ◽  
Wavelet Packet Decomposition ◽  
Precipitation Forecasting ◽  
Precipitation Prediction ◽  
Learning Machine

Abstract Accurate precipitation prediction can help plan for different water resources management demands and provide an extension of lead-time for the tactical and strategic planning of courses of action as well as activity. In this paper, a novel merged precipitation prediction framework (ELM-WPD) is proposed on the Extreme learning machine (ELM) with wavelet packet decomposition (WPD). The model can be described as the following: (a) we use the WPD to decompose the original precipitation data into several sub-layers; (b) ELM model is employed to complete the forecasting calculation for the decomposed series; (c) the results are integrated to complete the final prediction. Four quantitative indexes (RMSE, MAE, R and NSEC) are employed for the comparison criteria. The results are compared with Back-propagation neural network (BPNN), autoregressive integrated moving average model (ARIMA), ELM, BPNN-WPD model, ARIMA-WPD, indicating that the ELM-WPD model has better performance than other models used in this paper. Hence, the proposed method can provide higher accuracy and reliability for annual precipitation forecasting and can be extended to similar situations for precipitation forecasting.

scholarly journals Short-term rainfall forecast model based on the improved BP–NN algorithm

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Yang Liu ◽  
Qingzhi Zhao ◽  
Wanqiang Yao ◽  
Xiongwei Ma ◽  
Yibin Yao ◽  
...  
Keyword(s):  
Meteorological Parameter ◽  
Back Propagation ◽  
Precipitable Water ◽  
Forecast Model ◽  
Experimental Result ◽  
Atmospheric Parameters ◽  
Rainfall Forecasting ◽  
Short Term ◽  
Total Delay ◽  
Rainfall Forecast

AbstractThe 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.

scholarly journals ARIMAX model for rainfall forecasting in Pangkalpinang, Indonesia

2021 ◽  
Vol 926 (1) ◽  
pp. 012034
Author(s):  
R Amelia ◽  
D Y Dalimunthe ◽  
E Kustiawan ◽  
I Sulistiana
Keyword(s):  
Wind Speed ◽  
Arima Model ◽  
Daily Rainfall ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Maximum Wind Speed ◽  
Series Data ◽  
Rainfall Forecasting ◽  
Maximum Wind ◽  
Maximum Daily Rainfall

Abstract In recent years, the weather and climate are unpredictable and the most visible is the rotation of the rainy season and the dry season. The extreme changes in rainfall can cause disasters and losses for the community. For that we need to predict the rainfall to anticipate the worst events. Rainfall is included in the periodic series data, so the forecasting method that can be used is the ARIMAX model which is ARIMA model expanded by adding the exogen variable. The aim of this research is to predict the rainfall data in Pangkalpinang City, Indonesia. The best model for each rainfall is ARIMAX (0,1,3) for monthly rainfall data and ARIMAX (0,1,2) for maximum daily rainfall. This research shows that there is an influence maximum wind speed variable to monthly rainfall and maximum daily rainfall in the Pangkalpinang City. Nevertheless, when viewed from the ARIMA and ARIMAX models based on the obtained AIC value, the ARIMAX value is still better than ARIMA. However, the prediction value using ARIMAX needs to increase again to take into account seasonal data rainfall. Then, possible to add other exogeneous factors besides maximum wind speed.

Integrated AHP-BPNN Model for Wind Farm Investment Evaluation

2014 ◽  
Vol 541-542 ◽  
pp. 966-971
Author(s):  
Xiang Feng Zhang ◽  
Tian Yu Liu ◽  
Bin Jiao
Keyword(s):  
Wind Farm ◽  
Evaluation Method ◽  
Back Propagation ◽  
Wind Farms ◽  
Back Propagation Neural Network ◽  
Analytic Hierarchy ◽  
Farm Investment ◽  
Hierarchy Process ◽  
Power Project ◽  
Bpnn Model

The construction of wind farms grows quickly in China. It is necessary for stakeholders to estimate investment costs and to make good decisions about a wind power project by making a budget for the investment. This paper proposed an evaluation method by integrating the analytic hierarchy process (AHP) with back-propagation neural network (BPNN) to evaluate wind farm investment. In the AHP-BPNN model, the AHP method is used to determine the factors of wind farm investment. The factors with high importance are reserved while those with low importance are eliminated, which can decrease the number of inputs of the BPNN. The experiment results show that the integrated model is feasible and effective.

scholarly journals Adaptive frequency scaled wavelet packet decomposition for frog call classification

2016 ◽  
Vol 32 ◽  
pp. 134-144 ◽  
Author(s):  
Jie Xie ◽  
Michael Towsey ◽  
Jinglan Zhang ◽  
Paul Roe
Keyword(s):  
Wavelet Packet ◽  
Wavelet Packet Decomposition ◽  
Call Classification
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