The topic of climate change and the dangers that lie ahead is part of the debate in land management. The dynamics of global change and the sovereign approach of global governments have opened new perspectives on land management issues. One of the hidden challenges in this regard is the increasing risk of the occurrence of floods. The purpose of this study was to undertake metric or measurement model as a spatial basis unit to predict flood occurrence. In the present article, in line with using MikeUrban 2019 software, a wide range of tools and quantitative processing steps were used in accordance with the research objectives. In order to predict floods, the past incident factors were studied, namely a review of the principles and operational indicators related to each parcel using the OLI sensor images of Landsat 8 satellite in the year 2020 through the integrated interpretation method and an examination of the basic map of Ilam city in wet seasons (autumn, winter, and spring). Then, the city coverage map was prepared in two uses, i.e., False Color (Urban) and Land / Water. Next, Google Earth images were used to determine the accuracy and precision of the coverage maps. By combining four selected measures with the highest spatial correlation in 50 random points of the city, hexagonal measures with optimal areas were selected and spatial patterns were analyzed. According to the results, in the actual event (1), eight spatial measures with an area of ​​68 hectares and coverage of 1.5% of the entire city are at risk of flood. In events 0.8 and 0.9, 19 measures with an area of ​​170 hectares, 3.6% of the land use coverage of the area, are at potential risk. While there is a high correlation between flood event and type of measure, amount and direction of slope, as well as density and width of road network on one side and drainage network on the other side, in high events (1-8), 28 measures in the residential uses in the detailed design scale with an area of ​​76.5 hectares, barren and enclosed uses with an area of 70.55 hectares, roads network with an area of 29.75 hectares, and parks and green spaces with 17 hectares were identified as the uses targeted by the flood danger. Among all risky user groups, 29.5% were identified in the newly built group, 44.5% in the maintainable group, and 18% in the decayed urban environment group.

The land-use changes and urban development are among the fundamental topics of spatial planning. Monitoring changes in previous years and predicting these changes in the coming years have a significant role in planning and organizing urban spaces. The purpose of this study was to investigate land-use change and urban development in Ahvaz with a landscaping approach based on balanced urban development. .Images of TM (1989), ETM+ (2002), and OLI (2019) of the Landsat satellite are the basis for analyzing this trend. LCM model was used to identify the past changes, while CA-Markov chain model was applied to predict the future changes. These images were classified using a support vector machine algorithm of the object-oriented method, and the land-use maps were prepared using three sensors with four classes of vegetation, barren areas, constructed areas, and water zones. The accuracy of maps was improved separately using NDVI and SAVI indexes in the classification of the images. The efficiency of the indexes was measure by kappa coefficients and the overall accuracy of land-use maps, and then changes were investigated using maps related to the superior index. The results showed that maps related to the SAVI index were more accurate and accordingly, they were used in assessing land-use changes. The LCM model showed that in the periods 1989-2002 and 2002-2019, 2602.92 hectares and 31174.77 hectares were added to the built areas, respectively. In both periods, the most changes were about converting barren lands to built-up areas and the least changes were related to the transformation of the built-up areas to water areas. The results of the CA-Markov model until 2029 showed the continuity of the increasing trend of built-up areas, such that in ten years, 2238.82 hectares will be added to the built-up areas, and it is anticipated that the area will reach approximately 12345.63 hectares by 2029.