Time series analysis of remotely sensed water quality parameters in arid environments, Saudi Arabia

Mohamed Elhag (), Ioannis Gitas (), Anas Othman (), Jarbou Bahrawi (), Aris Psilovikos () and Nassir Al-Amri ()
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Mohamed Elhag: King Abdulaziz University
Ioannis Gitas: Aristotle University of Thessaloniki
Anas Othman: King Abdulaziz University
Jarbou Bahrawi: King Abdulaziz University
Aris Psilovikos: University of Thessaly
Nassir Al-Amri: King Abdulaziz University

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2021, vol. 23, issue 2, No 11, 1392-1410

Abstract: Abstract The monitoring of inland water resources in arid environments is an essential element due to their fragility. Reliable prediction of the water quality parameters helps to control and manage the water resources in arid regions. Water quality parameters were estimated using remote sensing data acquired from the beginning of 2017 until the end of 2018. The prediction of the water quality parameters was comprehended by using an adjusted autoregressive integrated moving average (ARIMA) and its extension seasonal ARIMA (S-ARIMA). Maximum Chlorophyll Index (MCI), Green Normalized Difference Vegetation Index (GNDVI) and Normalized Difference Turbidity Index (NDTI) were the tested water quality parameters using Sentinel-2 sensor on temporal resolution basis of the sensor. Results indicated that the implementation of the ARIMA model failed to sustain a reliable prediction longer than one-month time while S-ARIMA succeeded to maintain a robust prediction for the first 3 months with confidence level of 96%. MCI has its ARIMA at (1,2,2) and S-ARIMA at (1,2,2) (2,1,1)6, GNDVI has its ARIMA at (2,1,2) and S-ARIMA at (2,1,2) (2,2,2)6, and finally, NDTI has its ARIMA at (2,2,2) and S-ARIMA at (2,2,2) (1,1,2)6. The accuracy of S-ARIMA predictions reached 82% at 6-month prediction period. Meanwhile, there was no solid prediction model that lasted till 12 months. Each of the forecasted water quality parameters is unique in its prediction settings. S-ARIMA model is a more reliable model because the seasonality feature is inherited within the forecasted water quality parameters.

Keywords: ARIMA; Forecasting; Radiometric water indices; S-ARIMA; Seasonality (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10668-020-00626-z

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