International Journal of Advances in Science, Engineering and Technology(IJASEAT)
Follow Us On :
current issues
Volume-9,Issue-3  ( Jul, 2021 )
Statistics report
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
  Journal Paper

Paper Title :
Al-Hammar Marsh Restoration Strategy

Author :Faiz H. Al-Merib, Hanan Offie Jabber

Article Citation :Faiz H. Al-Merib ,Hanan Offie Jabber , (2019 ) " Al-Hammar Marsh Restoration Strategy " , International Journal of Advances in Science, Engineering and Technology(IJASEAT) , pp. 56-59, Volume-7,Issue-4

Abstract : Al- Hammar Marsh is considered as the largest marsh in the southern part of Iraq, it lies between the cities of Nasiriyah on the west side and Basrah on the southeast side, with an approximated area of 3000 km². Originally, Al-Hammar Marsh receives water from different branches on the right of Euphrates, in addition to the seasonal excess flow that flows from Al-Qurnah Marsh. The marsh outlets are transported to channels toward the Shatt Al-Arab. This study is an attempt to estimate the storage of Al- Hammar marsh and how water quantity can be raised. Data of effecting parameters (inflow, outflow, evaporation, transpiration, evapotranspiration) have been collected for five years (2013- 2017). Storage has been computed using the flood routing equation. In order to predict the future storage for the next five years, the effecting parameters are used to generate a predicted model using Artificial Neural Networks ANN. A general equation has been obtained, which can be applicable for variables data to compute the storage for any period time. With an accurate model implicating, semi-accurate results of predicted storage are presented, while the coefficient of determination (R2) and root mean square error (RMSE) of the predicted equation were (0.94, 0.43), respectively. The results show that future predicted storage quantities will increase as average to be (5.0* 109) m3 in comparing with the actual previous quantities which were (3.0 *109) m3. Keywords - Artificial Neural Networks, Flood Routing, Marsh Restoration

Type : Research paper

Published : Volume-7,Issue-4


Copyright: © Institute of Research and Journals

| PDF |
Viewed - 44
| Published on 2020-01-06
IRAJ Other Journals
IJASEAT updates
Volume-9,Issue-2(April, 2020)
The Conference World