Abstract
Water deficit problem originates from two factors: population increase and water pollution. However, studying and forecasting the quality of water are necessary to avoid serious problems in future through managerial works. In present study, using time series modeling, the quality of Madian Rood River is studied at Baraftab station using time series analysis. Nine parameters of water quality are studied such as: TDS, EC, HCO3-, Cl-, SO42+, Ca2+, Mg2+, Na+ and SAR. Investigation of observed time series shows that there is a common increasing trend for all parameters unless Na+ and SAR. The order of models for each parameter was determined using Auto Correlation Function (ACF) and Partial Auto Correlation Function (PACF) of time series. The ARIMA model was used to generate and forecast the quality of stream flows. Akaike Information Criterion (AIC), Determination Coefficient (R2), Root Mean Square Error (RMSE) and (Volume Error in Percent (VE %) criteria were referred to evaluate the generation and validation results. The Results show that time series modeling is quite capable of water quality forecasting. For the majority of forecasts, the value of R2 was greater than 0.6 between predicted and observed values.
Highlights
Water quality is a main subject of life due to its direct impact on human health
Time series of 9 water quality parameters such as TDS, EC, HCO3, Cl, SO42, Ca2+, Mg2+, Na+ and SAR of Baraftab station at Madian Rood River were studied in this research
Based on the field studies (JCE, 2005), the high growth and relative density of population, increasing the consumption of artificial stocks, leaving urban wastewaters and majority of rural sewage in traditional method through rivers, inconvenient methods of burying litters, dispersion of rubbishes and litters in surface waters and streams which inflow through rivers are considered as the major reasons of water quality deterioration
Summary
Water quality is a main subject of life due to its direct impact on human health. Water quality could be affected by geologic structure, salinity, overdraw of groundwater, urban and domestic wastewater entrance into surface streams as well as agricultural drainage and a wide range of chemical compounds (Tsakiris and Alexakis, 2012). Different methods and approaches are used to investigate and forecast the quality of water. Time series analysis is one of the useful methods which are applied in water quality modeling and forecasting. Time series analyses is useful in understanding and analyzing the process of different phenomena. It is helpful in generating past observations forecasting the future values based on the past memory. Time series is composed of a string of data over time with an equal interval between all data. The interval can be defined as daily, weekly, monthly as well as yearly time steps. Time series analysis in hydrology has two main goals: 1. Understand and model the stochastic mechanism of a hydrologic process and Time series analysis in hydrology has two main goals: 1. Understand and model the stochastic mechanism of a hydrologic process and
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