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

被引:23
|
作者
Elhag, Mohamed [1 ]
Gitas, Ioannis [2 ]
Othman, Anas [1 ]
Bahrawi, Jarbou [1 ]
Psilovikos, Aris [3 ]
Al-Amri, Nassir [1 ]
机构
[1] King Abdulaziz Univ, Dept Hydrol & Water Resources Management, Fac Meteorol Environm & Arid Land Agr, Jeddah 21589, Saudi Arabia
[2] Aristotle Univ Thessaloniki, Lab Forest Management & Remote Sensing, Sch Agr Forestry & Nat Environm, Thessaloniki 54124, Greece
[3] Univ Thessaly, Dept Ichthyol & Aquat Environm, Sch Agr Sci, Volos 38445, Magnesia, Greece
关键词
ARIMA; Forecasting; Radiometric water indices; S-ARIMA; Seasonality; MANAGEMENT; NESTOS; IMAGES; MSI;
D O I
10.1007/s10668-020-00626-z
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页码:1392 / 1410
页数:19
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