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Keywords = Amol city

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19 pages, 4669 KB  
Article
Assessing the Effect of Urban Growth on Surface Ecological Status Using Multi-Temporal Satellite Imagery: A Multi-City Analysis
by Mohammad Karimi Firozjaei, Naeim Mijani, Saman Nadizadeh Shorabeh, Yasin Kazemi, Yasser Ebrahimian Ghajari, Jamal Jokar Arsanjani, Majid Kiavarz and Seyed Kazem Alavipanah
ISPRS Int. J. Geo-Inf. 2023, 12(10), 406; https://doi.org/10.3390/ijgi12100406 - 4 Oct 2023
Cited by 2 | Viewed by 1990
Abstract
Quantification of Surface Ecological Status (SES) changes is of great importance for understanding human exposure and adaptability to the environment. This study aims to assess the effect of urban growth on spatial and temporal changes of SES over a set of neighboring Iranian [...] Read more.
Quantification of Surface Ecological Status (SES) changes is of great importance for understanding human exposure and adaptability to the environment. This study aims to assess the effect of urban growth on spatial and temporal changes of SES over a set of neighboring Iranian cities, Amol, Babol, Qaemshahr, and Sari, which are located in moderate and humid climate conditions. Firstly, the built-up footprint was prepared using Landsat images based on the Automatic Built-up Extraction Index (ABEI). Then, the surface biophysical characteristics were calculated. Secondly, the SES was modeled using the Remotely Sensed Ecological Index (RSEI), and the spatio-temporal changes of the SES were evaluated. The results revealed that the average RSEI for these cities increased from 0.48, 0.51, 0.53, and 0.55 in 1986 to 0.69, 0.77, 0.75, and 0.78 in 2022, respectively. The proportion of the poor ecological condition class in these cities rose from 10%, 3%, 5%, and 1% to 74%, 64%, 54%, and 41% during the 1986–2022 period. Our findings indicate that the SES of these cities significantly decreased while they experienced large physical growth. The findings and the methodical approach of the study provide a data-driven approach for monitoring SES in fast growing regions, which is required for studying the impact of climate change on society. Full article
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13 pages, 3829 KB  
Article
Urban Flood Hazard Modeling Using Self-Organizing Map Neural Network
by Omid Rahmati, Hamid Darabi, Ali Torabi Haghighi, Stefanos Stefanidis, Aiding Kornejady, Omid Asadi Nalivan and Dieu Tien Bui
Water 2019, 11(11), 2370; https://doi.org/10.3390/w11112370 - 12 Nov 2019
Cited by 52 | Viewed by 5990
Abstract
Floods are the most common natural disaster globally and lead to severe damage, especially in urban environments. This study evaluated the efficiency of a self-organizing map neural network (SOMN) algorithm for urban flood hazard mapping in the case of Amol city, Iran. First, [...] Read more.
Floods are the most common natural disaster globally and lead to severe damage, especially in urban environments. This study evaluated the efficiency of a self-organizing map neural network (SOMN) algorithm for urban flood hazard mapping in the case of Amol city, Iran. First, a flood inventory database was prepared using field survey data covering 118 flooded points. A 70:30 data ratio was applied for training and validation purposes. Six factors (elevation, slope percent, distance from river, distance from channel, curve number, and precipitation) were selected as predictor variables. After building the model, the odds ratio skill score (ORSS), efficiency (E), true skill statistic (TSS), and the area under the receiver operating characteristic curve (AUC-ROC) were used as evaluation metrics to scrutinize the goodness-of-fit and predictive performance of the model. The results indicated that the SOMN model performed excellently in modeling flood hazard in both the training (AUC = 0.946, E = 0.849, TSS = 0.716, ORSS = 0.954) and validation (AUC = 0.924, E = 0.857, TSS = 0.714, ORSS = 0.945) steps. The model identified around 23% of the Amol city area as being in high or very high flood risk classes that need to be carefully managed. Overall, the results demonstrate that the SOMN model can be used for flood hazard mapping in urban environments and can provide valuable insights about flood risk management. Full article
(This article belongs to the Special Issue Flash Floods in Urban Areas)
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