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DOI	10.1007/s00704-020-03419-6
	Forecasting monthly fluctuations of lake surface areas using remote sensing techniques and novel machine learning methods
	Soltani, Keyvan; Amiri, Afshin; Zeynoddin, Mohammad; Ebtehaj, Isa; Gharabaghi, Bahram; Bonakdari, Hossein
通讯作者	Bonakdari, H
来源期刊	THEORETICAL AND APPLIED CLIMATOLOGY
ISSN	0177-798X
EISSN	1434-4483
英文摘要	Drought is one of the most environmentally impactful hydrologic processes with devastating economic consequences for many rural communities in arid and semi-arid countries all over the world. In this research, we have employed satellite data and a stochastic approach for forecasting the changes in lake surface areas and demonstrated for the application of the new technique for the case study of the Lake Gregory in Australia. High-resolution Landsat satellite images are used on a monthly time scale from Landsat 5, 7, and 8, on days that are not cloudy. The software ENVI 5.3, using normalized difference vegetation index (NDVI), and modify normalized difference water index (MNDWI) indices were employed to obtain the lake surface maps, and satellite images have been split into water and non-water using a decision tree. The ArcGIS 10.3 software was used to calculate the area of the Lake monthly. The overall trend data shows that from 2004 to 2019, the LS is steadily declining, reaching its lowest area in 2019.The TRMM satellite monthly precipitation (P) and temperature (T) measurement were obtained to investigate the correlation between these changes and regional precipitation. We developed a novel generalized group method of data handling (GGMDH) to forecast lake surface (LS) fluctuations, in which the LS time-series database is extracted from the satellite imagery. For downscaling, precipitation and three different scenarios are defined based on climate change projections to forecast the LS in the 2020-2060 period. The comparison of the GGMDH with stochastic models integrated with preprocessing scenarios indicates the GGMDH in long-term LS forecasting outperforms the stochastic model. The result showed GGMDH is the best model among other ones to modeling lake surface by R-2 (%) = 94.16, RMSE = 8.77 for the forecasting stage. The forecasted surface of the Lake Gregory fluctuated from 226 to 0.008 km(2) in the future.
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000583103200001
WOS关键词	DIFFERENCE WATER INDEX ; SATELLITE DATA ; CLIMATE-CHANGE ; VEGETATION INDEX ; LANDSAT DATA ; ETM PLUS ; NDVI ; TM ; CALIBRATION ; WETLAND
WOS类目	Meteorology & Atmospheric Sciences
WOS研究方向	Meteorology & Atmospheric Sciences
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/328398
作者单位	[Soltani, Keyvan] Razi Univ, Dept Civil Engn, Kermanshah, Iran; [Amiri, Afshin] Univ Tehran, Dept Remote Sensing & GIS, Tehran, Iran; [Zeynoddin, Mohammad; Ebtehaj, Isa; Bonakdari, Hossein] Univ Laval, Dept Soils & Agrifood Engn, Laval, PQ G1V0A6, Canada; [Gharabaghi, Bahram] Univ Guelph, Sch Engn, Guelph, ON NIG 2W1, Canada
推荐引用方式 GB/T 7714	Soltani, Keyvan,Amiri, Afshin,Zeynoddin, Mohammad,et al. Forecasting monthly fluctuations of lake surface areas using remote sensing techniques and novel machine learning methods[J].
APA	Soltani, Keyvan,Amiri, Afshin,Zeynoddin, Mohammad,Ebtehaj, Isa,Gharabaghi, Bahram,&Bonakdari, Hossein.
MLA	Soltani, Keyvan,et al."Forecasting monthly fluctuations of lake surface areas using remote sensing techniques and novel machine learning methods".THEORETICAL AND APPLIED CLIMATOLOGY