Prediction lake level variations using satellite observations in Caspian Sea: Case study

M. Imani, R. J. You, C. Y. Kuo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The demand for accurate predictions of sea level fluctuations in coastal management and ship navigation activities is increasing. To meet such demand, accessible high-quality data and proper modeling process are critically required. In this study, we successfully present the analysis and forecasting of Caspian Sea level anomalies based on about 15-year Topex/Poseidon and Jason-1 altimetry data covering 1993-2008, which are originally developed and optimized for open oceans but have the considerable capability to monitor inland water level changes. The forecast is performed by Holt-Winters exponential smoothing (HWES) and multi-layer perceptron (MLP) neural network as alternative methods to the conventional models to assess their applicability for estimating Caspian Sea level anomalies. The modeling results agree well with the observed time series and satisfactorily present reliable results for the short-term prediction of Caspian Sea level anomalies providing reasonable precision and accuracy for supporting water reservoir management plans.

Original languageEnglish
Title of host publication34th Asian Conference on Remote Sensing 2013, ACRS 2013
PublisherAsian Association on Remote Sensing
Pages1710-1715
Number of pages6
ISBN (Print)9781629939100
Publication statusPublished - 2013 Jan 1
Event34th Asian Conference on Remote Sensing 2013, ACRS 2013 - Bali, Indonesia
Duration: 2013 Oct 202013 Oct 24

Publication series

Name34th Asian Conference on Remote Sensing 2013, ACRS 2013
Volume2

Other

Other34th Asian Conference on Remote Sensing 2013, ACRS 2013
CountryIndonesia
CityBali
Period13-10-2013-10-24

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

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