Niazmardi, Saeid, Alizadeh Naeini, Amin, Homayouni, Saeid, Safari, Abdolreza, Samadzadegan, Farhad (2013). Optimization of KFCM Clustering of Hyperspectral Data by Particle Swarm Optimization Algorithm. , 20(2), 79-100.
Saeid Niazmardi; Amin Alizadeh Naeini; Saeid Homayouni; Abdolreza Safari; Farhad Samadzadegan. "Optimization of KFCM Clustering of Hyperspectral Data by Particle Swarm Optimization Algorithm". , 20, 2, 2013, 79-100.
Niazmardi, Saeid, Alizadeh Naeini, Amin, Homayouni, Saeid, Safari, Abdolreza, Samadzadegan, Farhad (2013). 'Optimization of KFCM Clustering of Hyperspectral Data by Particle Swarm Optimization Algorithm', , 20(2), pp. 79-100.
Niazmardi, Saeid, Alizadeh Naeini, Amin, Homayouni, Saeid, Safari, Abdolreza, Samadzadegan, Farhad Optimization of KFCM Clustering of Hyperspectral Data by Particle Swarm Optimization Algorithm. , 2013; 20(2): 79-100.

Optimization of KFCM Clustering of Hyperspectral Data by Particle Swarm Optimization Algorithm

The International Journal of Humanities
Article 5, Volume 20, Issue 2, 2013, Pages 79-100    PDF (372.03 K)
Authors
Saeid Niazmardi; Amin Alizadeh Naeini; Saeid Homayouni; Abdolreza Safari; Farhad Samadzadegan
Abstract
Geographic information and analysis provide a wide range of data and techniques to monitor and manage natural resources. As an important case, in arid and semi-arid areas, water management is critical for both local governance and citizens. As a result, the estimation of water potential brought by snowmelt runoff and rainfalls seems to be very useful and important for these areas. Hydrological modeling needs vast knowledge about integrating all relating parameters. In this work, different data sources including the remote sensing observations, meteorological and geological data are integrated to supply spatially detailed inputs for Snowmelt Runoff Modeling in a watershed, located in Simin-Dasht basin in the northeast of Tehran, Iran. Because of high temporal frequency and suitable spatial coverage, MODIS optical images have been chosen to map snow cover. The MODIS 8-day snow map product with spatial resolution of 500m (MOD10A2.5) is used to compute the snow cover area. In addition, during the snowmelt period in 2006-2007, archived meteorological and geological data are used to provide snow runoff modeling (SRM) parameters and variables. Also Landsat ETM+ images with better spatial resolution (30m) and less temporal coverage (16 days) are used in 2007 snowmelt period to compare the model accuracy with same conditions. Evaluation of the runoff outputs in both of models reveals good agreement with real data that prove SRM capability in modeling basin’s daily and weekly runoff. Model accuracy shows better satisfactory of snow runoff modeling results within snow cover area derived from Landsat ETM+ data and MODIS snow product was less accurate in modeling. Although using MODIS model accuracy was less, but still it is recommended due to less further process and providing better temporal coverage during snowfall and snowmelt season. Future works in this criterion could be concentrated on SRM forecast improvement using fusion with other measurements or combining physical models.
Keywords
Snowmelt Runoff Modeling; Optical Remotely Sensed Images; Snow Cover Area; Meteorological data; KFCM; PSO
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