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A comparison of the estimated effective elastic thickness of the lithosphere using terrestrial and satellite-derived data in Iran

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Abbaszadeh M. , Sharifi M. , Nikkhoo M.

Acta Geophysica

2013

tom Vol. 61, no. 3

638--648

The effective elastic thickness of the lithosphere has an important role in constraining compositional structure, geothermal gradient and tectonic forces within the lithosphere and the thickness of this layer can be used to evaluate the earthquakes’ focal depth. Hence, assessment of the elastic thickness of the lithosphere by gravitational admittance method in Iran is the main objective of this paper. Although the global geopotential models estimated from the satellite missions and surface data can portray the Earth’s gravity field in high precision and resolution, there are some debates about using them for lithosphere investigations. We used both the terrestrial data which have been provided by NCC (National Cartographic Center of Iran) and BGI (Bureau Gravimetrique International), and the satellite-derived gravity and topography which are generated by EIGEN-GL04C and ETOPO5, respectively. Finally, it is concluded that signal content of the satellite-derived data is as rich as the terrestrial one and it can be used for the determination of the lithosphere bending.

A Proposed Model to Forecast Hourly Global Solar Irradiation Based on Satellite Derived Data, Deep Learning and Machine Learning Approaches

75%

Benamrou B. , Ouardouz M. , Allaouzi I. , Ben Ahmed M.

Journal of Ecological Engineering

2020

tom Vol. 21, nr 4

26--38

An accurate short-term global solar irradiation (GHI) forecast is essential for integrating the photovoltaic systems into the electricity grid by reducing some of the problems caused by the intermittency of solar energy, including rapid fluctuations in energy, management storage, and the high costs of electricity. In this paper, the authors proposed a new hybrid approach to forecast hourly GHI for the Al-Hoceima city, Morocco. For this purpose, a deep long short-term memory network is trained on a combination of the hourly GHI ground measurements from the meteorological station of Al-Hoceima and the satellite-derived GHI from the neighbouring pixels of the point of interest. Xgboost, Random Forest, and Recursive Feature Elimination with cross-validation were used to select the most relevant features, the lagged satellite-derived GHI around the point of interest, as input to the proposed model where the best forecasting model is selected using the Grid Search algorithm. The simulation and results showed that the proposed approach gives high performance and outperformed other benchmark approaches.