Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques

By C. Wu, Kwok-Wing Chau, C. Fan. 2010

362 citations recorded by the source corpus.

This study is an attempt to seek a relatively optimal data-driven model for rainfall forecasting from three aspects: model inputs, modeling methods, and data-preprocessing techniques. Four rain data records from different regions, namely two monthly and two daily series, are examined. A comparison of seven input techniques, either linear or nonlinear, indicates that linear correlation analysis (LCA) is capable of identifying model inputs reasonably. A proposed model, modular artificial neural network (MANN), is compared with three benchmark models, viz. artificial neural network (ANN), K-nearest-neighbors (K-NN), and linear regression (LR). Prediction is performed in the context of two modes including normal mode (viz., without data preprocessing) and data preprocessing mode. Results from the normal mode indicate that MANN performs the best among all four models, but the advantage of MANN over ANN is not significant in monthly rainfall series forecasting. Under the data preprocessing mode, each of LR, K-NN and ANN is respectively coupled with three data-preprocessing techniques including moving average (MA), principal component analysis (PCA), and singular spectrum analysis (SSA). Results indicate that the improvement of model performance generated by SSA is considerable whereas those of MA or PCA are slight. Moreover, when MANN is coupled with SSA, results show that advantages of MANN over other models are quite noticeable, particularly for daily rainfall forecasting. Therefore, the proposed optimal rainfall forecasting model can be derived from MANN coupled with SSA.

Subfields: Electrical and Electronic Engineering, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/513969abb237d2e33e05bbc562488d53173ecf17

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

In Environmental Engineering

In Global and Planetary Change

Citations in the corpus

Wavelet Analysis-Support Vector Machine Coupled Models for Monthly Rainfall Forecasting in Arid Regions (2015, methodology): “…inference system (ANFIS), and support vector machine (SVM) have been applied to rainfall forecasting (French et al. 1992; Valverde Ramírez et al. 2005; Aksoy and Dahamsheh 2008; Dahamsheh and Aksoy 2008; Srivastava et al. 2010; Wu et al. 2010; Moustris et al. 2011; Kashid and Maity 2012).”
Assessment of evolutionary algorithms in predicting non-deposition sediment transport (2016, methodology): “Due to the high capacity of soft computation in analyzing complicated problems, these methods have been extensively applied, during the past decades, in water resources and hydraulic structures (Baghalian et al., 2012; Bonakdari et al., 2011; Kisi, 2008; Lin et al., 2006; Muzzammil, 2008; Tayfur & G”
A hybrid support vector regression–firefly model for monthly rainfall forecasting (2018, background): “Nowadays, artificial intelligence (AI) methods such as artificial neural network (ANN), fuzzy logic (FL), support vector machine (SVM), and genetic programming (GP) are widely used for rainfall forecasting (e.g., Aksoy and Dahamsheh 2009; Nourani et al. 2009; Wu et al. 2010; El-Shafie et al. 2011; M”
Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model (2017, methodology): “…discrete wavelet transformation (DWT) in water resources research has grown in recent years (Adamowski and Karapataki 2010; Adamowski and Sun 2010; Wu et al. 2010; Kisi and Cimen 2011; Tiwari and Chatterjee 2011; Adamowski and Chan 2011; Tiwari and Adamowski 2013; Nourani et al. 2014; Bachour et…”