Mustafa Al-Mukhtar

Bio: Mustafa Al-Mukhtar is an academic researcher from University of Technology, Iraq. The author has contributed to research in topics: Medicine & Pan evaporation. The author has an hindex of 9, co-authored 24 publications receiving 266 citations. Previous affiliations of Mustafa Al-Mukhtar include Freiberg University of Mining and Technology.

The relationship between haematocrit and some biological parameters of the Indian shad, Tenualosa ilisha (Family Clupeidae)

Abstract: Jawad, L. A., Al-Mukhtar, M. A. & Ahmed, H. K., 2004. The relationship between haematocrit and some biological parameters of the Indian shad, Tenualosa ilisha (Family Clupeidae). Animal Biodiversity and Conservation, 27.2: 47-52. Abstract The relationship between haematocrit and some biological parameters of the Indian shad, Tenualosa ilisha (Family Clupeidae).— Haematological parameters have been recognised as valuable tools for the monitoring of fish health. Here we analyse the relationship between haematocrit and body length, sex and reproductive state in the Indian Shad Tenualosa ilisha. Haematocrit value showed a quadratic relationship to fish size (body length), incrementing as the fish body length increased up to 400 mm, after which it decreased. Male fish showed a higher haematocrit value than females. Haematocrit appeared to be higher in the pre-spawning period than in the spawning phase, but then increased slightly in the post- spawning period.

Modeling Water Quality Parameters Using Data-Driven Models, a Case Study Abu-Ziriq Marsh in South of Iraq

Abstract: Total dissolved solids (TDS) and electrical conductivity (EC) are important parameters in determining water quality for drinking and agricultural water, since they are directly associated to the concentration of salt in water and, hence, high values of these parameters cause low water quality indices. In addition, they play a significant role in hydrous life, effective water resources management and health studies. Thus, it is of critical importance to identify the optimum modeling method that would be capable to capture the behavior of these parameters. The aim of this study was to assess the ability of using three different models of artificial intelligence techniques: Adaptive neural based fuzzy inference system (ANFIS), artificial neural networks (ANNs) and Multiple Regression Model (MLR) to predict and estimate TDS and EC in Abu-Ziriq marsh south of Iraq. As so, eighty four monthly TDS and EC values collected from 2009 to 2018 were used in the evaluation. The collected data was randomly split into 75% for training and 25% for testing. The most effective input parameters to model TDS and EC were determined based on cross-correlation test. The three performance criteria: correlation coefficient (CC), root mean square error (RMSE) and Nash–Sutcliffe efficiency coefficient (NSE) were used to evaluate the performance of the developed models. It was found that nitrate (NO3), calcium (Ca+2), magnesium (Mg+2), total hardness (T.H), sulfate (SO4) and chloride (Cl−1) are the most influential inputs on TDS. While calcium (Ca+2), magnesium (Mg+2), total hardness (T.H), sulfate (SO4) and chloride (Cl−1) are the most effective on EC. The comparison of the results showed that the three models can satisfactorily estimate the total dissolved solids and electrical conductivity, but ANFIS model outperformed the ANN and MLR models in the three performance criteria: RMSE, CC and NSE during the calibration and validation periods in modeling the two water quality parameters. ANFIS is recommended to be used as a predictive model for TDS and EC in the Iraqi marshes.

Random forest, support vector machine, and neural networks to modelling suspended sediment in Tigris River-Baghdad.

Abstract: Suspended sediment is one of the most influential parameters on the water bodies’ pollution. It can carry different pollutants with different concentration through the suspension movement in the flow. Therefore, it is of utmost importance to monitoring or modelling these loads so that an accurate sediment reduction strategy can be adopted. However, the monitoring process is laborious and time-consuming task. Thus, modelling is suggested as an alternative method. In this study, three different methods of artificial intelligence (i.e., random forest, support vector machine (Radial Basis Function), and artificial neural network) were employed to model and predict the suspended load at Sarai Station in Baghdad. To this end, observed flow rate (m3/s) and the corresponding suspended sediment concentration (mg/l) measured over the periods 1962–1981 and 2000–2010 were collected. Auto and partial correlation was used to identify the best combinations of input model data. The data was randomly partitioned into 75% for training and 25% for validation. The confidence interval was hypothesized to assess the uncertainty in the observed and predicted data. Whereas, the k-fold cross validation was used to quantify the uncertainty in the modelling results. The predictive modelling results for the three evaluated methods were assessed based on R2, RMSE, and NSE coefficient. Results show that random forest has the superior performance among the others. The total suspended sediment transported was estimated to be 72,734,852 ton during the period 2000–2010.

Assessing the Impacts of Climate Change on Hydrology of the Upper Reach of the Spree River: Germany

Abstract: The aim of this study was to assess the potential impacts of future climate change on the hydrological response in the upper reach of the Spree River catchment using the Soil and Water Assessment Tool (SWAT). The model was calibrated for ten years (1997–2006) and validated with the data from four years (2007–2010) using average monthly stream flow. The impact of future climate change on precipitation, temperature, evapotranspiration and stream flow was then investigated from two different downscaled climate models (CLM and WETTREG2010) under SRES A1B scenarios for two future periods (2021–2030 and 2041–2050). Besides that, sensitivity analysis was carried out with and without observations, to test robustness of the sensitivity algorithm used in the model. Results of the determination coefficient R2 and Nasch-Sutcliff efficiency ENC were 0.81 and 0.80, respectively, during the calibration; 0.71 and 0.70, respectively, during the validation. Although some parameters were changed their sensitiveness ranking when the model run with observations, the SWAT model was, however, able to predict the top influential parameters without observations. According to 12 realizations from the two downscaled climate models, annual stream flow from 2021–2030 (2041–2050) is predicted to decrease by 39 % (43 %). This corresponds to an increase in annual evapotranspiration from 2021–2030 (2041–2050) of 36 % (38 %). The upper reach of the Spree River catchment will likely experience a significant decrease in stream flow due to the increasing in the evapotranspiration rates. This study could be of use for providing insight into the availability of future stream flow, and to provide a planning tool for this area.

Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils

Abstract: Research highlights? The use of multiple regression (MR), artificial neural network (ANN) and artificial neuro-fuzzy inference system (ANFIS) models, for the prediction of swell percent of soils, was described and compared. ? However the accuracies of ANN and ANFIS models may be evaluated relatively similar, it is shown that the constructed ANN models of RBF and MLP exhibit a high performance than ANFIS and multiple regression for predicting swell percent of clays. ? The performance comparison showed that the soft computing system is a good tool for minimizing the uncertainties in the soil engineering projects. ? The use of soft computing will also may provide new approaches and methodologies, and minimize the potential inconsistency of correlations. In the recent years, new techniques such as; artificial neural networks and fuzzy inference systems were employed for developing of the predictive models to estimate the needed parameters. Soft computing techniques are now being used as alternate statistical tool. Determination of swell potential of soil is difficult, expensive, time consuming and involves destructive tests. In this paper, use of MLP and RBF functions of ANN (artificial neural networks), ANFIS (adaptive neuro-fuzzy inference system) for prediction of S% (swell percent) of soil was described, and compared with the traditional statistical model of MR (multiple regression). However the accuracies of ANN and ANFIS models may be evaluated relatively similar. It was found that the constructed RBF exhibited a high performance than MLP, ANFIS and MR for predicting S%. The performance comparison showed that the soft computing system is a good tool for minimizing the uncertainties in the soil engineering projects. The use of soft computing will also may provide new approaches and methodologies, and minimize the potential inconsistency of correlations.