Search

The occurrence of dam failures and the consequent floods pose substantial risks to submerged regions, and their unpredictability increases the hazard of their impact. This research simulates the flood event scenario due to the dam failure of the Hemrem dam in Diyala Governorate, Iraq. HEC-RAC model jas been used to modeling the Hemren dam failure. The generated flood waves are routed downstream until the confluence of the Diyala and Tigris rivers, covering a large study area of 5,378.5 km². The configuration of the model's geometry is formulated using the Digital Elevation Model (DEM) that encompasses the geographical extent of the Diyala River study area, from Hemren Dam to the confluence with the Tigris River south of Baghdad. The length of this reach is approximately 210 km. Based on the inundation maps, the region's geography is classified as having catastrophic constraints due to the water depth and flow rate. The results demonstrate how to predict the magnitude of floods and highlight the severity if Hemren Dam were to fail. This underscores the need for effective risk management. Furthermore, the maps created by this study can be utilized to develop long-term flood control plans.

In this research, the potential improvement of some geotechnical characteristics of soft clay soil using the low Calcium fly ash was evaluated. (These characteristics include unit weight, shear strength, compaction characteristics and soil plasticity characteristics). In addition, the X-ray diffraction test was performed to measure the mineralogical changes in the soft clay soil when the low Calcium fly ash is added. The ordinary Portland cement was used to activate the fly ash. The total percent of flash and cement was10% to investigate the variation in the effectiveness of activation. The optimum moisture content that which computed by the compaction test was adopted in the rest of the experimental program. The test results revealed that the cement could be used to improve the activating of the fly ash efficiently. The maximum value of dry density was marginally affected due to activation from 1.747 to 1.738 g/cm3 along with a corresponding change in optimum water content from 17.45 to 15.5 %. The soil cohesion parameter increased from 188 to 206 kN/m2 whereas the angle of internal friction rose from about 56.7o to 59.1o. Finally, the results of the unconfined compression test reveal that the cement-activated fly ash could present better results than those obtained from a 28-days curing cement.

The prediction of different hydrological phenomenon (or system) plays an increasing role in the management of water resources. As engineers; it is required to predict the component of natural reservoirs’ inflow for numerous purposes. Resulting prediction techniques vary with the potential purpose, characteristics, and documented data. The best prediction method is of interest of experts to overcome the uncertainty, because the most hydrological parameters are subjected to the uncertainty. Artificial Neural Network (ANN) approach has adopted in this paper to predict Hemren reservoir inflow. Available data including monthly discharge supplied from DerbendiKhan reservoir and rain fall intensity falling on the intermediate catchment area between Hemren-DerbendiKhan dams were used. A Back Propagation (LMBP) algorithm (Levenberg-Marquardt) has been utilized to construct the ANN models. For the developed ANN model, different networks with different numbers of neurons and layers were evaluated. A total of 24 years of historical data for interval from 1980 to 2004 were used to train and test the networks. The optimum ANN network with 3 inputs, 40 neurons in both two hidden layers and one output was selected. Mean Squared Error (MSE) and the Correlation Coefficient (CC) were employed to evaluate the accuracy of the proposed model. The network was trained and converged at MSE = 0.027 by using training data subjected to early stopping approach. The network could forecast the testing data set with the accuracy of MSE = 0.031. Training and testing process showed the correlation coefficient of 0.97 and 0.77 respectively and this is refer to a high precision of that prediction technique.

The evaporation is one of the basic components of the hydrologic cycle and is essential for studies such as water balance, irrigation system design, and water resource management and it is requires knowledge of the values of many climatic variables. In order to estimate the evaporation, direct measurement methods or physical and empirical models can be used. Using direct methods require installing meteorological stations and instruments for measuring evaporation. Installing such instruments in various areas requires specific facilities and cost which is hard to be employed. Accordingly, this paper is an attempt to assess the potential and usefulness of ANN based modeling for evaporation prediction from Hemren reservoir by using daily temperature, relative humidity, wind velocity, sunshine hours, and evaporation data in Hemren meteorological station. Also, this study outlines a procedure to evaluate the effects of input variables on the output variable using the weight connections of ANN models. The Lev. Marqn. Back Prog. (LMBP) has been utilized to construct the ANN models. For the development ANN model, different networks with different numbers of neurons and layers were evaluated. Mean Squared Error (MSE) and the Correlation Coefficient (R2) were employed to evaluate the accuracy of the proposed model. The study shows that the best model for estimation of evaporation is ANN (4-10-1), it have MSE equal to 0.112711 and the correlation coefficient (R2) equal to 0.999540.

In this study Water Quality Index (WQI) was applied in Hemren Lake, Diyala province, Iraq using ten water quality parameters (pH, Electrical Conductivity, Hardness, Total Dissolve Soluble, Sodium, Calcium, Magnesium, Potassium, Chloride, Phosphate) from 2008 to 2010 to evaluate the suitability of Hemren Lake ecosystem for drinking and irrigation uses. The Weighted Arithmetic Index method (WAM) and the Canadian Council of Ministers of the Environment Water Quality Index methodology (The CWQI 1.0 model) were used to calculate the water quality index (W.Q.I). The results indicated that drinking water quality of Hemren Lake is good and marginal for the study period according to (WAM) and (CCME) respectively, while the irrigation water quality is good and according to (WAM) and (CCME). It is suggested that monitoring of the lake is necessary for proper management. Application of the WQI is also suggested as a very helpful tool that enables the public and decision makers to evaluate water quality of lakes in Iraq.