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Change information of the earth’s surface is becoming more and more important in monitoring the local, regional and global resources and environment. In this paper, two algorithms maximum likelihood (ML) and artificial neural network (ANN) have been applied for a change detection at the Baher Al-Najaf from 2016 to 2020 and using two satellite imagery Landsat and sentinel. The accuracy assessment of these algorithms was based on the confusion matrix such as overall accuracy, and the kappa index were calculated for each created map. It was found (ANN) classifier is the better than (ML) classifier. As well as two different experiments were conducted to analyze the network depth and optimization of ANN classifier, and the results showed that the proposed classifier achieves higher overall accuracy and kappa index with pansharpening image. Finally, this study proved (ANN) classifier ability to extract useful high-level features in the classification process.

The cross printed dipoles antenna is the best solution to generate wide band circular polarization, in this design prepared cross rectangular printed dipoles (antenna1) then added four inner quadrants parasitic (antenna2) with perfect arrangement and excellent dimensions. The quadrants, (inner parasitic) rotate sequentially round the crossed dipole, are used to persuade new resonance and increase the activity bandwidth, additional improving the AR bandwidth. The results show that the antenna achieves an impedance bandwidth (IBW) of 88.38% (4.66-12.04 GHz) for |S11| < −10 dB and a wide axial ratio bandwidth (ARBW) of 55.80% (5.75-10.2 GHz). The proposed antenna would be utilized in C-band applications and for wireless local area network (WLAN) applications. The structures modelled and analysed by the Ansoft’s HFSS simulation software.

the last four years have seen a remarkable rise in water levels to the Bahar-Al- Najaf, which led to some areas adjacent to the Al-Bahar region. This creates a state of confusion in the subject of future planning for land uses within the study area. To examine this problem in practice, the images of the Sentinel-2 satellite (within the period 2016-2020) were selected. The resulted thematic maps included six classes of land use and land cover LULC, these classes are (agricultural areas, built up areas, gypsum soil, clay soil, sandy soil, and water areas). With regard to the accuracy of the performed classification process, the so-called confusion matrix indicates that the overall classification accuracy is about 85%. The study concluded that some of the important land use, both the road pilgrimage, the strategic line and its oil transfer accessories had been overwhelmed by water, as well as water approaches a newly built residential complex near the Bahar Al- Najaf. It is imposed by the study; the federal and local government agencies must take rapid measures and procedures to avoid potential future flood risk.