A novel deseasonalized time series model with an improved seasonal estimate for groundwater level predictions

Balaji Narasimhan,S Mohanasundaram,G Suresh Kumar

doi:10.2166/h2oj.2019.022

Balaji Narasimhan, S Mohanasundaram + Show 1 more

Open Access

https://doi.org/10.2166/h2oj.2019.022

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Abstract

Abstract Groundwater level prediction and forecasting using univariate time series models are useful for effective groundwater management under data limiting conditions. The seasonal autoregressive integrated moving average (SARIMA) models are widely used for modeling groundwater level data as the groundwater level signals possess the seasonality pattern. Alternatively, deseasonalized autoregressive and moving average models (Ds-ARMA) can be modeled with deseasonalized groundwater level signals in which the seasonal component is estimated and removed from the raw groundwater level signals. The seasonal component is traditionally estimated by calculating long-term averaging values of the corresponding months in the year. This traditional way of estimating seasonal component may not be appropriate for non-stationary groundwater level signals. Thus, in this study, an improved way of estimating the seasonal component by adopting a 13-month moving average trend and corresponding confidence interval approach has been attempted. To test the proposed approach, two representative observation wells from Adyar basin, India were modeled by both traditional and proposed methods. It was observed from this study that the proposed model prediction performance was better than the traditional model's performance with R2 values of 0.82 and 0.93 for the corresponding wells' groundwater level data.

Highlights

Hydrological systems are widely studied using models that describe and relate various physical processes
The specific objectives of this study are: (1) to construct the confidence interval for irregularly varying groundwater level signal based on 13-month moving average trend values to estimate the seasonal component and (2) to compare the prediction performance of the proposed deseasonalized ARMA (DARMA) model against the traditional DARMA and seasonal autoregressive integrated moving average (SARIMA) models
In SARIMA models, a seasonal differencing was done to change the nonstationary series to a stationary series

Summary

Introduction

Hydrological systems are widely studied using models that describe and relate various physical processes. Conceptual or physical models are the key tools to understand and predict the variable responses of the system (Anderson & Woessner 1992). A time series model is an empirical model for stochastically simulating and forecasting the behavior of uncertain hydrologic systems (Chatfield & Weigend 1994; Sannasiraj et al 2004; Kim et al 2005). This kind of model is often useful to model any hydrological system such as groundwater system for groundwater level fluctuation modeling and forecasting where there is limited availability of additional auxiliary data sets

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