Hybrid SSA-ARIMA-ANN Model for Forecasting Daily Rainfall
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Hybrid SSA-ARIMA-ANN Model for Forecasting Daily Rainfall Poornima Unnikrishnan 1 & V. Jothiprakash 2 Received: 28 February 2020 / Accepted: 22 July 2020/ # Springer Nature B.V. 2020
Abstract
Rainfall, which is one of the most important hydrologic processes, is influenced by many meteorological factors like climatic change, atmospheric temperature, and atmospheric pressure. Even though there are several stochastic and data driven hydrologic models, accurate forecasting of rainfall, especially smaller time step rainfall forecasting, still remains a challenging task. Effective modelling of rainfall is puzzling due to its inherent erratic nature. This calls for an efficient model for accurately forecasting daily rainfall. Singular Spectrum Analysis (SSA) is a time series analysis tool, which is found to be a very successful data pre-processing algorithm. SSA decomposes a given time series into a finite number of simpler and decipherable components. This study proposes integration of Singular Spectrum Analysis (SSA), Auto Regressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) into a hybrid model (SSA-ARIMAANN), which can yield reliable daily rainfall forecasts in a river catchment. In the present study, spatially averaged daily rainfall data over Koyna catchment, Maharashtra has been used. In this study SSA is proposed as a data pre-processing tool to separate stationary and non-stationary components from the rainfall data. Correlogram and Kwiatkowski– Phillips–Schmidt–Shin (KPSS) test has been used to validate the stationary and nonstationary components. In the developed hybrid model, the stationary components of rainfall data are modelled using ARIMA method and non-stationary components are modelled using ANN. The study of statistical performance of the model shows that the hybrid SSA-ARIMA-ANN model could forecast the daily rainfall of the catchment with reliable accuracy. Keywords Hybrid SSA-ARIMA-ANN model . Singular Spectrum analysis . ARIMA . ANN . Daily rainfall forecasting . Stationary components . Non-stationary components . KPSS test
* V. Jothiprakash [email protected] Poornima Unnikrishnan [email protected] Extended author information available on the last page of the article
Unnikrishnan P., Jothiprakash V.
1 Introduction Development of reliable and accurate hydrologic time series models has gained the attention of researchers over past few decades. The changing climatic phenomenon and its impact on the ecosystem have made it imperative to develop an accurate hydrologic model with the help of which, water resources systems can be better managed. Out of the several hydrologic models, the mostly used are those relying on either stochastic methods or Artificial Intelligence (AI) technique which have their own advantages and disadvantages. In stochastic modelling, forecasts are deciphered based on the statistical characteristics of the past data (Box and Jenkins 1976). ARIMA model, which is the most widely used stochastic model for forecasting time series, ha
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