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Volume 1, Issue 2 (7-2014)
Flood pron areas of rivers are generally hazardous. Regionalizing these hazardous areas in terms of the degree of hazard they produce is very important for regional flood management, insurance companies and land users. Therefore, this research has tried to regionalize the potential hazard of the flood prone areas of the Morghak River using HEC-GeoRAS model as an example for all flood plains of Gillan province.
In order to develop the hydrolic model of the river, the following data were prepared.
- The river profile, roughness index of the river channel and flood plain and river bank conditions were obtained from 1:2000 TIN maps.
- The data were entered into the HEC-RAS model.
- Then the data of the river banks and flood discharge amounts were entered and hydraulic computations were carried out.
- The model results were entered into the GIS. After the requested processing in the extension of HEC-GeoRas431, the final maps of depth of river, water movement velocity, shear velocity and the flow intensity along the river channel were produced.
- The maps were moved into the Google Earth and the flood area with different return periods were plotted.
The results showed the areal expansion of the 25-year return period floods of the river basin. This plain is narrow in the upper areas of the river and widens in the lower areas of the area. In the areas that there are constructions, the basin gets wider and its higher discharges causes severe hazards in the settlements around the river. The widest part of the flood plain is over the lowlands around Anzali swamp. In these lowlands the flood spreads over the vast area and making problems for the farmers and dwellers.
According to the results of this research some adaptation measures are needed in the areas where people have moved to the river channel and have built some structures. Some of these measures include vegetation planting, cement and rocky barriers and cleaning all extra wastes. The results of the study also indicate that in most of the branches the building of the channel has narrowed the channel and caused flood in the settled areas. The physiographic parameters of the river have seriously been changed and caused the severe floods in the river especially in the lower areas. The flow speed of the river changes from 4.1 m/s in the maximum discharge to .2 m/s in the very low discharge. The width of the channel has also changed from 281 meters in the maximum to 11 meters at the low discharge period. The discharge stress was between .3 to 357 newtons the overall results of the research indicate that the human interference in the river basin has caused all these hazards. And the only solution is that the humans should go out of the risky areas of the river basin. The continuation of this process in this river or in the other rivers will worsen the present hazards,
Volume 1, Issue 3 (10-2014)
Iran is a wide and great land that is located on Alps earthquake belt of Himalaya. Great part of the urban and village residency of the country have been exposed to the intensive earthquake and destructive. Sarab city with several other cities, including Tehran, Karaj, Abyek, Qazvin, Roudbar, Khalkhal ,Tabriz, Marand and khoy are located on Earthquake belt that Earthquake risk is too high. In Eastern-Azerbaijan and Sarab, potential earthquake risk is very high, since there are a lot of active faults and historical evidences show the horrific and destructive earthquakes. Sarab city located in the Sarab plain which have abundant faults in various directions. Earthquake as a natural phenomenon doesn’t have good results but what can make it a catastrophe, is the lack of prevention from its effects and no preparation for coping with its aftermaths. The unsuitable establishment of structural elements and urban land-uses and atypical web of urban open spaces, the old ages of and low quality of the structures in the decayed area of the factors like this have main role in the increasing the amount of damage entered to the cities against to the earthquake. It is necessary to reduce the vulnerability of the cities against the earthquake and to consider it as one of the main goals of the urban planning. Main objective of this paper is planning for reduction of damages arising out of earthquake in Sarab city. The study area is the Sarab city with four urban regions and 15 districts. The present research is an applied study. For this purpose, considering the goal of the study, nine factors including the type of structural materials, the quality of the buildings, the number of the floors, the population density, pedestrian width , the availability of open space and distance from river were identified and evaluated, so that for each of the indicators or factors, one layer of map with shp format was produced and then in an analytical hierarchy process and weighting to the variables, layers overlaying operation using available analytical functions was implemented in Arc Gis software. Finally the vulnerability map of the Sarab city was prepared. According to the results of AHP model, it is concluded that Sarab in terms of vulnerability has no appropriate status against earthquake risk so that the whole Sarab city is vulnerable to earthquake, but some of its neighborhoods due to low quality of buildings and vulnerability of streets network and inaccessibility to open areas and excessive compression are more vulnerable. Deteriorated urban area is one of the most vulnerable parts of the Sarab city during the occurrence of the earthquake. Therefore, to reduce the health and wealth damages which can cause by the earthquake in the city.
Volume 4, Issue 3 (9-2017)
I conducted all spatial analysis in the UTM Zone 39 Northern Hemisphere projection. The fundamental procedure I used for evaluating change in land surface temperature was to relative temperature for both images, so that the values are temperature difference between the coldest and hottest areas in Tehran metropolitan. subtracting these images from each other results in relative temperature change from 2003 to 2015. Landsat satellite data were used to extract land use/land cover information and their changes for the abovementioned cities. Land surface temperature was retrieved from Landsat thermal images. The relationship between land surface temperature and landuse /land-cover classes, as well as the normalized vegetation index (NDVI) was analyzed.
In this study, LST for Tehran metropolitan was derived using SW algorithm with the use of Landsat 8 Optical Land Imager (OLI) of 30 m resolution and Thermal Infrared Sensor (TIR) data of 100 m resolution. SW algorithm needs spectral radiance and emissivity of two TIR bands as input for deriving LST. The spectral radiance was estimated using TIR bands 10 and 11. Emissivity was derived with the help of land cover threshold technique for which OLI bands 2, 3, 4 and 5 were used. The output revealed that LST was high in the barren regions whereas it was low in the hilly regions because of vegetative cover. As the SW algorithm uses both the TIR bands (10 and 11) and OLI bands 2, 3, 4 and 5, the LST generated using them were more reliable and accurate. NDVI negatively affected LST and Urban Heat Island in vegetation areas in 2003 and 2015 in Tehran metropolitan. This analysis provides an effective tool in evaluating the environmental influences of zoning in urban ecosystems with remote sensing and geographical information systems. This method exhibits a promising performance in UHI forecast. The predicted LST confirms that urban growth has severely influenced UHI pattern through expanding the hot area. Our study confirmed that LST prediction performance is strongly depended on the resolution.
The results reveal that the urban LST is affected mainly by the land surface characteristics and has a close relation to the abundance of vegetation greenness. The spatial distance from the UHI centre is another important factor influencing the LST in some areas. The methodology presented in this paper can be broadly applied in other metropolitans which exhibit a similar dynamic growth. Our findings can represent a useful tool for policy makers and the community awareness of environmental assessment by providing a scientific basis for sustainable urban planning and management. This provides an effective tool in evaluating the vegetation greenness of different zoning in urban ecosystems with remote sensing and geographical information systems. From the perspective of land use planning and urban management, it is recommend that planners and policy makers should pay serious attention to future land use policies that maintain a relevant proportion of public space, green areas, and land surface physical characteristics.
Volume 10, Issue 1 (5-2023)
Introduction
A flood is a natural disaster caused by heavy rainfall, which causes casualties and damage to infrastructure and crops. Trend of floods in the world increasing due to climate change, changing rainfall patterns, rising sea levels in the future, and in addition, population growth and urban development and human settlements near river have caused floods to become a threat to humans. One of the most important and necessary tasks in catchments is to prepare flood risk maps and analyze them. In recent decades, researchers have been using remote sensing techniques and geographic information systems to obtain flood risk maps in an area. Due to the numerous floods that have occurred in the Neka river catchment, it is necessary to conduct a study entitled zoning of flood sensitivity in Neka river catchment for more effective management in this area.
Materials and methods
Study area: Neka river catchment area with an area of 1922
To study the flood zoning of the area using a multi-criteria decision model, 1: 25000 maps of the surveying organization and a digital elevation model with a resolution of 12.5 meters (Alos Palsar) were extracted. In order to study the flood risk in Neka river, 4 criteria of height, distance from the river, land use and slope have been used. In the present study, modeling and preparation of flood risk zoning map in 4 stage including descending valuation, normalization of each class, normalized index weight and integration of criteria has been done by the following linear weighting method. Performing linear weighting operations depends on the weighted average of a number of selected parameters in the opinion of the expert. According to the weight assigned to each criterion based on the expert opinion, each of the criteria was multiplied by the assigned weight and at the end the criteria were added together and the final zoning map was obtained.
Results and Discussion
In this study, using a multi-criteria decision-making system model, a flood risk zoning map in the Neka river catchment was prepared. According to the weight assigned to each criterion based on expert opinion, the final risk probability map has a value between 0.02 to 0.2, which is ultimately divided into 5 classes in terms of flood risk. Value range 0.02 to 0.06 component of very low risk zone, range 0.08 to 0.11 component of low-risk zone, range 0.11 to 0.13 component of medium-risk zone, range 0.13 to 0.16 component of high-risk zone, and finally domain 0.16 to 0.20 components of the area with very high risk potential have been obtained. According to the final divisions in the flood risk zoning map of the catchment area, a safe area means areas where the probability of flooding is very low and close to zero, and in contrast, the area with a high and very high risk potential for flooding has the probability of high-risk floods. According to the final flood risk zoning map, about 982
Conclusion
The results obtained from the model indicates that the highest risk of flooding points are located in the western parts of the Neka catchment area and the end of the catchment area that reach the city of Neka. According to the research findings, the most important factors in increasing the risk of floods were the slope in this area and the distance from the drainage network. According to the results of the model, a large area of the basin is a component of high risk zone, that means the Neka river watershed has a high potential for floods. Evidence and documented reports show that the Neka river Basin has experienced several floods in the last two decades. The major part of the occurrence of floods is due to the natural conditions of the basin, thus it is necessary to reduce flood damage by changing the locations of various land uses based on flood vulnerability maps. Using multi-criteria decision making method can be used to prepare flood risk zoning maps in basins that do not have hydrometric data; It is also a more cost-effective method in terms of time. One of the important issues in the final result of this model is due to the weight of the layers, which should be used by experts, who are familiar with the region and this method and adapt to field evidence.
Keyworlds: Flood, Multi-criteria decision making system(MCDA), Hazard zoning, Nekarod, Natural hazard.
Volume 11, Issue 1 (5-2024)
Nowadays, deep learning as a branch of artificial intelligence acts as an alternative for human with hopeful outcomes. Open Street Map as the biggest open source data is used as a complementary data sources for spatial projects. It is notable that is some advanced counties the accuracy of VGI data is higher than governmental official data. This research aims to use artificial intelligence to produce and subsequently promote completeness of OSM data. Res_UNet architecture was utilized to train landuse categories to the network. The result shows that IoU metric is about 83 percent that implies a high accuracy paradigm. Then, united-based method was used to calculated completeness of OSM data. The unit-based results show that completeness of building blocks, forest, fruits garden and agriculture land are: 3.6, 9.7, 90.4 and 81.88 respectively. It shows the low volunteer participation rate to produce OSM data. On the other side the high accuracy achieved by deep learning leads us to complete OSM data by artificial intelligence instead of human prepared data. The advantage of using machine rather than human may be utilized in undeveloped countries or low density population regions as well as inaccessible areas.
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