Monitoring and Predicting Temporal Changes of Urmia Lake and its Basin Using Satellite Multi-Sensor Data and Deep-Learning Algorithms View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2022-04-04

AUTHORS

Ali Radman, Mehdi Akhoondzadeh, Benyamin Hosseiny

ABSTRACT

In this study, Urmia lake and its basin, which are vital regions in the northwest of Iran, were monitored using satellite data and modeling methods. Monthly precipitation was computed using TRMM satellite dataset. Terrestrial Water Storage (TWS), evaporation, temperature, and TWS Anomaly (TWSA) were estimated from GLDAS dataset and GRACE missions. Moreover, Jason satellite altimetry series and MODIS were used to assess the lake Water Level (WL) and area variations. These seven parameters were estimated from April 2002 to June 2019. This study adopted and evaluated four deep-learning methods based on feed-forward and recurrent architectures for data modeling, and, subsequently, predicting the water area variations. According to the obtained results, Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) models had some malfunctions in predicting lake area, while Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) acquired results close to real variations of Urmia lake area. Taking Mean Absolute Error, Mean Relative Error, Root Mean Squared Error (RMSE), and correlation coefficient (r) as evaluation parameters, LSTM achieved the superior quantities, 175.07 km2, 18.87%, 231.7 km2, and 0.83, respectively. Results also indicate that LSTM is more accurate while predicting the variation of critical situations. More... »

PAGES

319-335

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s41064-022-00203-1

DOI

http://dx.doi.org/10.1007/s41064-022-00203-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1146862075


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