scholarly journals Linking Singular Spectrum Analysis and Machine Learning for Monthly Rainfall Forecasting

2020 ◽  
Vol 10 (9) ◽  
pp. 3224 ◽  
Author(s):  
Pa Ousman Bojang ◽  
Tao-Chang Yang ◽  
Quoc Bao Pham ◽  
Pao-Shan Yu
Keyword(s):  
Machine Learning ◽  
Water Resources ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Hybrid Models ◽  
Monthly Rainfall ◽  
Support Vector ◽  
Rainfall Forecasting ◽  
Singular Spectrum ◽  
Standard Models

Monthly rainfall forecasts can be translated into monthly runoff predictions that could support water resources planning and management activities. Therefore, development of monthly rainfall forecasting models in reservoir watersheds is essential for generating future rainfall amounts as an input to a water-resources-system simulation model to predict water shortage conditions. This research aims to examine the reliability of linking a data preprocessing method (singular spectrum analysis, SSA) with machine learning, least-squares support vector regression (LS-SVR), and random forest (RF), for monthly rainfall forecasting in two reservoir watersheds (Deji and Shihmen reservoir watersheds) located in Taiwan. Merging SSA with LS-SVR and RF, the hybrid models (SSA-LSSVR and SSA-RF) were developed and compared with the standard models (LS-SVR and RF). The proposed models were calibrated and validated using the watersheds’ observed areal monthly rainfalls separated into 70 percent of data for calibration and 30 percent of data for validation. Model performances were evaluated using two accuracy measures, root mean square error (RMSE) and Nash–Sutcliffe efficiency (NSE). Results show that the hybrid models could efficiently forecast monthly rainfalls. Nonetheless, the performances of the hybrid models vary in both watersheds which suggests that prior knowledge about the watershed’s hydrological behavior would be helpful to implement the appropriate model. Overall, the hybrid models significantly surpass the standard models for the two studied watersheds, which indicates that the proposed models are a prudent modeling approach that could be employed in the current research regions for monthly rainfall forecasting.

scholarly journals An innovated integrated model using Singular Spectrum Analysis and Support Vector Regression optimized by intelligent algorithm for rainfall forecasting

2019 ◽  
Vol 2 (1) ◽  
pp. 46
Author(s):  
Weide Li ◽  
Juan Zhang
Keyword(s):  
Support Vector Regression ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Support Vector ◽  
Monthly Precipitation ◽  
Rainfall Forecasting ◽  
Dragonfly Algorithm ◽  
Singular Spectrum ◽  
Droughts And Floods ◽  
Precipitation Anomalies

Rainfall forecasting is becoming more and more significant and precipitation anomalies would lead to droughts and floods disasters. However, because of the complexity and non-stationary of rainfall data, it is difficult to forecast. In this paper, a novel hybrid model to forecast rainfall is developed by incorporating singular spectrum analysis (SSA) and dragonfly algorithm (DA) into support vector regression (SVR) method. Firstly, SSA is used for extracting the trend components of the hydrological data. Then, SVR is utilized to deal with the volatility and irregularity of the precipitation series. Finally, the parameter of SVR is optimized by DA. The proposed SSA-DA-SVR method is used to forecast the monthly precipitation for Songbai, Panshui, Lanma and Jiulongchi stations. To validate the efficiency of the method, four compared models, DA-SVR, SSA-GWO-SVR, SSA-PSO-SVR, SSA-CS-SVR are established. The result shows the proposed method has the best performance among all five models, and its prediction has high precision and accuracy.

Application of an ensemble technique based on singular spectrum analysis to daily rainfall forecasting

2003 ◽  
Vol 16 (3-4) ◽  
pp. 375-387 ◽  
Author(s):  
Daniela Baratta ◽  
Giovambattista Cicioni ◽  
Francesco Masulli ◽  
Léonard Studer
Keyword(s):  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Daily Rainfall ◽  
Rainfall Forecasting ◽  
Ensemble Technique ◽  
Singular Spectrum

scholarly journals A hybrid model coupled with singular spectrum analysis for daily rainfall prediction

2010 ◽  
Vol 12 (4) ◽  
pp. 458-473 ◽  
Author(s):  
K. W. Chau ◽  
C. L. Wu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Regression ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Daily Rainfall ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Singular Spectrum ◽  
Artificial Neural

A hybrid model integrating artificial neural networks and support vector regression was developed for daily rainfall prediction. In the modeling process, singular spectrum analysis was first adopted to decompose the raw rainfall data. Fuzzy C-means clustering was then used to split the training set into three crisp subsets which may be associated with low-, medium- and high-intensity rainfall. Two local artificial neural network models were involved in training and predicting low- and medium-intensity subsets whereas a local support vector regression model was applied to the high-intensity subset. A conventional artificial neural network model was selected as the benchmark. The artificial neural network with the singular spectrum analysis was developed for the purpose of examining the singular spectrum analysis technique. The models were applied to two daily rainfall series from China at 1-day-, 2-day- and 3-day-ahead forecasting horizons. Results showed that the hybrid support vector regression model performed the best. The singular spectrum analysis model also exhibited considerable accuracy in rainfall forecasting. Also, two methods to filter reconstructed components of singular spectrum analysis, supervised and unsupervised approaches, were compared. The unsupervised method appeared more effective where nonlinear dependence between model inputs and output can be considered.

Sign in / Sign up

Export Citation Format

Share Document