Skills
Artificial Intelligence Modelling:

The Artificial Intelligence Modelling is significantly used nowadays for the simulation of the hydrological processes based on available observational data that are required for the application of the related physical laws.

Different Applications of Microsoft Office, Matlab,:

The ability to use MS Office applications is vital in engineering and teaching proficiency. The office applications have been basically designed by Microsoft to carry out different office tasks smoothly.

Membership
Jan, 2004 - Current
Kurdistan Engineers Union (KEU)

Civil Consultant Engineer

Erbil

Dec, 1983 - Current
Iraqi Engineers Union (IEU)

Consultant Engineer

Mosul

Publication Journal
Jan, 2016
Empirical Penman-Monteith Equation and Artificial Intelligence Techniques in Predicting Reference Evapotranspiration: A Review

International Journal of Water (Issue: 101:55-66) (Volume: 10)

Evapotranspiration is a fundamental requirement in the planning and management of irrigation projects. Methods of predicting evapotranspiration (ET) are numerous, but the Food and Agriculture Organization (FAO) of the United Nations adopted the FAO Penman-Monteith (PM) equation, as the method which provides the most accurate results for the prediction of reference evapotranspiration (ET0) in all regions and for all weather conditions. The main identified drawback in the application of this method is the wide variety of weather parameters required for the prediction. To overcome this problem, artificial neural networks (ANNs) models have been proposed to simulate the nonlinear, dynamic ET0 processes. This paper highlights both the traditional empirical PM method, and the enhancement obtained by the utilisation of ANN techniques in predicting ET0.

Aug, 2015
Extreme Learning Machines : A New Approach for Prediction of Reference Evapotranspiration

Journal of Hydrology (Issue: 527(c), 184–195) (Volume: 527)

Recognizing the importance of precise determination of reference evapotranspiration (ET0) is a principal step in the attempts to reserve huge quantities of squandered water. This paper investigates the efficiency of Extreme Learning Machines (ELM) algorithm at predicting Penman–Monteith (P–M) ET0 for Mosul, Baghdad, and Basrah meteorological stations, located at the north, mid, and southern part of Iraq. Data of weather parameters containing maximum air temperature (Tmax), minimum air temperature (Tmin), sunshine hours (Rn), relative humidity (Rh), and wind speed (U2) for the period (2000–2013) are used as inputs to the ELM model by using four different input cases including complete and incomplete sets of meteorological data. The performance of ELM model is compared with the empirical P–M equation and with feedforward backpropagation (FFBP) model. The evaluation criteria used for comparison are the root of mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The statistical results of both models are found to be encouraging; particularly results of running the ELM model with incomplete sets of data, noticing that the sensitivity of the proposed model to missing data changes from one location to another, as well as along the year for certain study location. The Rn is found to be the most effective parameter in Mosul Station, while U2 and Rh are found to act almost in parallel with Rn in Baghdad Station, and for conditions of Basrah Station; U2 and Rh prove to be the dominant parameters. The minimum and maximum time intervals required for running ELM model for all stations, and in all applied conditions, are (4.64–6.19) seconds respectively, while the same order of timing required for running the FFBP model is (6.30–27.80) seconds. The maximum R2 recorded for the ELM model is 0.991, while for the FFBP it is 0.985. The ELM proved to be efficient, simple in application, of high speed, and has very good generalization performance; therefore, this algorithm is highly recommended for locations similar to the geographical and meteorological conditions of Iraq that consists of both arid and semiarid regions.

Jul, 2015
Feedforwad Backpropagation, Genetic Algorithm Approaches for Predicting Reference Evapotranspiration

Sains Malaysiana (Issue: 7(2015): 1053–1059) (Volume: 44)

Water scarcity is a global concern, as the demand for water is increasing tremendously and poor management of water resources will accelerates dramatically the depletion of available water. The precise prediction of evapotranspiration (ET), that consumes almost 100% of the supplied irrigation water, is one of the goals that should be adopted in order to avoid more squandering of water especially in arid and semiarid regions. The capabilities of feedforward backpropagation neural networks (FFBP) in predicting reference evapotranspiration (ET0) are evaluated in this paper in comparison with the empirical FAO Penman-Monteith (P-M) equation, later a model of FFBP+Genetic Algorithm (GA) is implemented for the same evaluation purpose. The study location is the main station in Iraq, namely Baghdad Station. Records of weather variables from the related meteorological station, including monthly mean records of maximum air temperature (Tmax), minimum air temperature (Tmin), sunshine hours (Rn), relative humidity (Rh) and wind speed (U2), from the related meteorological station are used in the prediction of ET0 values. The performance of both simulation models were evaluated using statistical coefficients such as the root of mean squared error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). The results of both models are promising, however the hybrid model shows higher efficiency in predicting ET0 and could be recommended for modeling of ET0 in arid and semiarid regions.

Jun, 2014
Hybrid of Artificial Neural Network-Genetic Algorithm for Prediction of Reference Evapotranspiration (ET0) in Arid and Semiarid Regions

Journal of Agricultural Science (Issue: 3, 191–200) (Volume: 6)

Evapotranspiration is a principal requirement in designing any irrigation project, especially in arid and semiarid regions. Precise prediction of Evapotranspiration would reduce the squandering of huge quantities of water. Feedforward Backpropagation Neural Network (FFBPNN) model is employed in this study to evaluate the performance of Artificial Neural Networks (ANNs) in comparison with Empirical FAO Penman-Monteith (P-M) Equation in predicting reference evapotranspiration (ETo); later, a hybrid model of ANN-Genetic Algorithm (GA) is proposed for the same evaluation function. Daily averages of maximum air temperature (Tmax), minimum air temperature (Tmin), relative humidity (Rh), radiation hours (R), and wind speed (U2) from Mosul station (Nineveh, Iraq) are used as inputs to the ANN simulation model to predict ET? values obtained using P-M Equation. The main performance evaluation functions for both models are the Mean Square Errors (MSE) and the Correlation Coefficient (R2). Both models yield promising results, but the hybrid model shows a higher efficiency in prediction of Evapotranspiration and could be recommended for modeling ET? in arid and semiarid regions.