Arid
DOI10.1007/s10668-020-00626-z
Time series analysis of remotely sensed water quality parameters in arid environments, Saudi Arabia
Elhag, Mohamed1; Gitas, Ioannis2; Othman, Anas1; Bahrawi, Jarbou1; Psilovikos, Aris3; Al-Amri, Nassir1
通讯作者Elhag, Mohamed
来源期刊ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY
ISSN1387-585X
EISSN1573-2975
出版年2020
英文摘要

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.


英文关键词ARIMA Forecasting Radiometric water indices S-ARIMA Seasonality
类型Article ; Early Access
语种英语
国家Saudi Arabia ; Greece
收录类别SCI-E
WOS记录号WOS:000515704000003
WOS关键词MANAGEMENT ; NESTOS ; IMAGES ; MSI
WOS类目Green & Sustainable Science & Technology ; Environmental Sciences
WOS研究方向Science & Technology - Other Topics ; Environmental Sciences & Ecology
资源类型期刊论文
条目标识符http://119.78.100.177/qdio/handle/2XILL650/314405
作者单位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
推荐引用方式
GB/T 7714
Elhag, Mohamed,Gitas, Ioannis,Othman, Anas,et al. Time series analysis of remotely sensed water quality parameters in arid environments, Saudi Arabia[J],2020.
APA Elhag, Mohamed,Gitas, Ioannis,Othman, Anas,Bahrawi, Jarbou,Psilovikos, Aris,&Al-Amri, Nassir.(2020).Time series analysis of remotely sensed water quality parameters in arid environments, Saudi Arabia.ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY.
MLA Elhag, Mohamed,et al."Time series analysis of remotely sensed water quality parameters in arid environments, Saudi Arabia".ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY (2020).
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