Research & Supervision

Researches:

- "Optimal Dimensions of Small Hydraulic Structure Cutoffs Using Coupled Genetic Algorithm and ANN Model", Journal of Engineering, Volume 20, Number 2, pages 1-19, University of Baghdad, February 2014, Baghdad, Iraq.

http://www.iasj.net/iasj?func=issueTOC&isId=5097&uiLanguage=ar

Abstruct:

A genetic algorithm model coupled with artificial neural network model was developed to find the optimal values of upstream, downstream cutoff lengths, length of floor and length of downstream protection required for a hydraulic structure. These were obtained for a given maximum difference head, depth of impervious layer and degree of anisotropy. The objective function to be minimized was the cost function with relative cost coefficients for the different dimensions obtained. Constraints used were those that satisfy a factor of safety of 2 against uplift pressure failure and 3 against piping failure. Different cases reaching 1200 were modeled and analyzed using geo-studio modeling, with different values of input variables. The soil was considered homogeneous anisotropic. For each case, the length of protection (L) and the volume of the superstructure (V) required to satisfy the factors of safety mentioned above were calculated. These data were used to obtain an artificial neural network model for estimating (L) and (V) for a given length of upstream cutoff (S1), length of downstream cutoff (S2), head difference (H), length of floor (B), depth of impervious layer (D) and degree of anisotropy (kx/ky). A MatLAB code was written to perform a genetic algorithm optimization modeling using the obtained ANN model .The obtained optimum solution for some selected cases were compared with the Geo-studio modeling to find the length of protection required in the downstream side and volume required for superstructure. Values estimated were found comparable to the obtained values from the Genetic Algorithm model.

- "Spatial Prediction of Monthly Precipitation in Sulaimani Governorate using Artificial Neural Network Models" , Journal of Engineering, Volume 20, Number 3, pages 15-27, University of Baghdad, March 2014, Baghdad, Iraq.

http://www.iasj.net/iasj?func=issueTOC&isId=5271&uiLanguage=ar

Abstruct:

ANN modeling is used here to predict missing monthly precipitation data in one station of the eight weather stations network in Sulaimani Governorate. Eight models were developed, one for each station as for prediction. The accuracy of prediction obtain is excellent with correlation coefficients between the predicted and the measured values of monthly precipitation ranged from (90% to 97.2%). The eight ANN models are found after many trials for each station and those with the highest correlation coefficient were selected. All the ANN models are found to have a hyperbolic tangent and identity activation functions for the hidden and output layers respectively, with learning rate of (0.4) and momentum term of (0.9), but with different data set sub-division into training, testing and holdout data sub-sets, and different number of hidden nodes in the hidden layer. It is found that it is not necessary that the nearest station to the station under prediction has the highest effect; this may be attributed to the high differences in elevation between the stations. It can also found that the variance is not necessary has effect on the correlation coefficient obtained

- "Daily inflow forecasting for Dukan reservoir in Iraq using artificial neural networks", International Journal of Water, Volume 9, Number 2, pages 194-208, Genève, Switzerland.

http://www.inderscience.com/info/inarticle.php?artid=68961

Abstruct:

Five ANN model versions were developed for the daily inflow forecasting to Dukan reservoir in Iraq. These models are dependent on the preceding days' lags (1, 2, 3, 4, and 5), respectively. The model versions forecasting correlation coefficients were found to be (94.6%, 94.6%, 95.2%, 95%, and 73%), respectively. The third model was used for forecasting and found capable of forecasting long term daily inflow series of the Dukan reservoir. Moreover it was found also capable of preserving the high and low persistences of this series in addition to the perfect simulation of the recession part and time to peak of the hydrograph.

I supervise the project(s) below:

• Hydraulic and Structural Design of proposed culvert in Chwarta district.
• Re-design of Chamchamal - Bani Maqan Culvert.
• _________
• _________