ESTIMATING WATER LEVEL IN THE URMIA LAKE USING SATELLITE DATA: A MACHINE LEARNING APPROACH

Lakes play a pivotal role in the development of cities and have major impacts on the ecosystem balancing of the area. Remote sensing techniques and advanced modeling methods make it possible to monitor natural phenomena, such as lakes’ water level. The ecosystem of Urmia Lake is one of the most mome...

Full description

Bibliographic Details
Main Authors: M. Boueshagh, M. Hasanlou
Format: Article
Language:English
Published: Copernicus Publications 2019-10-01
Series:The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Online Access:https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-4-W18/219/2019/isprs-archives-XLII-4-W18-219-2019.pdf
id doaj-de35ba463dec4066a1d35a19a59b588d
record_format Article
spelling doaj-de35ba463dec4066a1d35a19a59b588d2020-11-25T01:06:46ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342019-10-01XLII-4-W1821922610.5194/isprs-archives-XLII-4-W18-219-2019ESTIMATING WATER LEVEL IN THE URMIA LAKE USING SATELLITE DATA: A MACHINE LEARNING APPROACHM. Boueshagh0M. Hasanlou1School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, IranSchool of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, IranLakes play a pivotal role in the development of cities and have major impacts on the ecosystem balancing of the area. Remote sensing techniques and advanced modeling methods make it possible to monitor natural phenomena, such as lakes’ water level. The ecosystem of Urmia Lake is one of the most momentous ecosystems in Iran, which is almost close-ended and has become a global environmental issue in recent years. One of the parameters affecting this lake water level is snowfall, which has a key role in the fluctuations of its water level and water resources management. Hence, the purpose of this paper is the Urmia Lake water level estimation during 2000–2006 using observed water level, snow cover, direct precipitation, and evaporation. For this purpose, Support Vector Regression (SVR), which is the most outstanding kernel method (with various kernel types), has been used. Furthermore, four scenarios are considered with different variables as inputs, and the output of all scenarios is the water level of the lake. The results of training and testing data indicate the substantial impact of snow on retrieving the water level of the Urmia Lake at the desired period, and due to the complexity of the data relationships, the Gaussian kernel generally had better results. On the other hand, Quadratic and Cubic kernels did not work well. The fourth scenario, with RBF kernel has the best results [Training: R<sup>2</sup>&thinsp;=&thinsp;97% and RMSE&thinsp;=&thinsp;0.09&thinsp;m, Testing: R<sup>2</sup>&thinsp;=&thinsp;96.97% and RMSE&thinsp;=&thinsp;0.08&thinsp;m].https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-4-W18/219/2019/isprs-archives-XLII-4-W18-219-2019.pdf
collection DOAJ
language English
format Article
sources DOAJ
author M. Boueshagh
M. Hasanlou
spellingShingle M. Boueshagh
M. Hasanlou
ESTIMATING WATER LEVEL IN THE URMIA LAKE USING SATELLITE DATA: A MACHINE LEARNING APPROACH
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
author_facet M. Boueshagh
M. Hasanlou
author_sort M. Boueshagh
title ESTIMATING WATER LEVEL IN THE URMIA LAKE USING SATELLITE DATA: A MACHINE LEARNING APPROACH
title_short ESTIMATING WATER LEVEL IN THE URMIA LAKE USING SATELLITE DATA: A MACHINE LEARNING APPROACH
title_full ESTIMATING WATER LEVEL IN THE URMIA LAKE USING SATELLITE DATA: A MACHINE LEARNING APPROACH
title_fullStr ESTIMATING WATER LEVEL IN THE URMIA LAKE USING SATELLITE DATA: A MACHINE LEARNING APPROACH
title_full_unstemmed ESTIMATING WATER LEVEL IN THE URMIA LAKE USING SATELLITE DATA: A MACHINE LEARNING APPROACH
title_sort estimating water level in the urmia lake using satellite data: a machine learning approach
publisher Copernicus Publications
series The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
issn 1682-1750
2194-9034
publishDate 2019-10-01
description Lakes play a pivotal role in the development of cities and have major impacts on the ecosystem balancing of the area. Remote sensing techniques and advanced modeling methods make it possible to monitor natural phenomena, such as lakes’ water level. The ecosystem of Urmia Lake is one of the most momentous ecosystems in Iran, which is almost close-ended and has become a global environmental issue in recent years. One of the parameters affecting this lake water level is snowfall, which has a key role in the fluctuations of its water level and water resources management. Hence, the purpose of this paper is the Urmia Lake water level estimation during 2000–2006 using observed water level, snow cover, direct precipitation, and evaporation. For this purpose, Support Vector Regression (SVR), which is the most outstanding kernel method (with various kernel types), has been used. Furthermore, four scenarios are considered with different variables as inputs, and the output of all scenarios is the water level of the lake. The results of training and testing data indicate the substantial impact of snow on retrieving the water level of the Urmia Lake at the desired period, and due to the complexity of the data relationships, the Gaussian kernel generally had better results. On the other hand, Quadratic and Cubic kernels did not work well. The fourth scenario, with RBF kernel has the best results [Training: R<sup>2</sup>&thinsp;=&thinsp;97% and RMSE&thinsp;=&thinsp;0.09&thinsp;m, Testing: R<sup>2</sup>&thinsp;=&thinsp;96.97% and RMSE&thinsp;=&thinsp;0.08&thinsp;m].
url https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-4-W18/219/2019/isprs-archives-XLII-4-W18-219-2019.pdf
work_keys_str_mv AT mboueshagh estimatingwaterlevelintheurmialakeusingsatellitedataamachinelearningapproach
AT mhasanlou estimatingwaterlevelintheurmialakeusingsatellitedataamachinelearningapproach
_version_ 1725188311590371328