Volume 75 (2022)
Volume 74 (2021)
Volume 73 (2020)
Volume 72 (2019)
Volume 71 (2018)
Volume 70 (2017)
Volume 69 (2016)
Volume 68 (2015)
Volume 67 (2014)
Volume 66 (2013)
Volume 65 (2012)
Volume 63 (2010)
Volume 62 (2009)
Determining the most important geomorphometric parameters in classification of desert plains with using artificial neural networks and sensitivity analysis

Gholamreza Zehtabian; Hassan Ahmadi; Aliakbar Nazari Samani; amir houshang ehsani; Mahdi Tazeh

Volume 70, Issue 1 , June 2017, , Pages 197-206


  Plains are one of the most important geomorphological units and different parameters have been considered for classification of plain areas. One of most common classifications in natural resources studies in Iran entailing different qualitative and quantitative factors is: bare plains, apandazh plain ...  Read More

An optimized semi- automatic method for geomorphometric classification of Lut Yardangs using artificial neural etwork

amir houshang ehsani; Marzieh Foroutan

Volume 67, Issue 3 , December 2014, , Pages 359-380


  In this study the land surface in western half of hyper-arid Lut desert, in south east of Iran, which is covered by Yardangs, a worldwide typical landform for Aeolian erosion, were classified by Self Organizing Maps (SOM) method. In the first step by using Digital Elevation Model with 10 m resolution ...  Read More

Application of Artificial Neural Networks in Simulating and Forecasting of Meteorological Drought Decile Percentage Index (Case study: Sistan & Balouchestan Province)

Arash Malekian; Mahrou Dehbozorgi; Amir Houshang Ehsani; Amir Reza Keshtkar

Volume 67, Issue 1 , May 2014, , Pages 127-139


  Consecutive droughts in Sistan and Baloochestan province cause water resources restriction and this isa very significant problem for this region. In this study, in order to forecast the drought cycle in 9climatological stations in the province, we used Artificial Neural Networks. The input data wereaverage ...  Read More