Land Cover Classification of Mosul Planning Using Gray Wolf Algorithm

The land cover is constantly exposed on location to some changes as a result of natural effects such as floods, earthquakes, and fires caused by high temperatures or as a result of artificial effects such as population tides caused by wars, etc., leading to the need to provide the classification of land cover on a regular schedule. In this paper, a new technique presented for classifying the land cover in the Mosul province using the Gray Wolf Algorithm (GWA). The GWA is a part of an intelligence swarm for efficiency algorithm that derives inspired from the hunting method of gray wolves. Our goal is to increase the classification accuracy rate and the efficiency of the classification of land cover in Mosul planning region by using the GWA’s abilities. this method will participate the progress of remote sensing techniques, which can help environment management, disaster risk reduction, and land use planning. The gray wolf algorithm was implemented using MATLAB, and five classes were identified from the image. Each class (water, soils, outcrops, slope deposits, and accumulation glaciers) had a specific colour to serve as a class indicator. Good results were done, and the accuracy was calculated after comparing it with real data, yielding a percentage of 90.56%.