Mapping flood-prone areas is a key activity in flood disaster management. In this paper, we propose a new flood susceptibility mapping technique. We employ new ensemble models based on bagging as a meta-classifier and K-Nearest Neighbor (KNN) coarse, cosine, cubic, and weighted base classifiers to spatially forecast flooding in the Haraz watershed in northern Iran. We identified flood-prone areas using data from Sentinel-1 sensor. We then selected 10 conditioning factors to spatially predict floods and assess their predictive power using the Relief Attribute Evaluation (RFAE) method. Model validation was performed using two statistical error indices and the area under the curve (AUC). Our results show that the Bagging–Cubic–KNN ensemble model outperformed other ensemble models. It decreased the overfitting and variance problems in the training dataset and enhanced the prediction accuracy of the Cubic–KNN model (AUC=0.660). We therefore recommend that the Bagging–Cubic–KNN model be more widely applied for the sustainable management of flood-prone areas.
Although traditional cellular automata (CA)-based models can effectively simulate urban land-use changes, they typically ignore the spatial evolution of urban patches, due to their use of cell-based simulation strategies. This research proposes a new patch-based CA model to incorporate a spatial constraint based on the growth patterns of urban patches into the conventional CA model for reducing the uncertainty of the distribution of simulated new urban patches. In this model, the growth pattern of urban patches is first estimated using a developed indicator that is based on the local variations in existing urban patches. The urban growth is then simulated by integrating the estimated growth pattern and land suitability using a patterncalibrated method. In this method, the pattern of new urban patches is gradually calibrated toward the dominant growth pattern through the steps of the CA model. The proposed model is applied to simulate urban growth in the Tehran megalopolitan area during 2000-2006-2012.The results from this model were compared with two common models: cell-based CA and logistic-patch CA. The proposed model yields a degree of patch-level agreement that is 23.4 and 7.5% higher than those of these pre-existing models, respectively. This reveals that the patch-based CA model simulates actual development patterns much better than the two other models.
Cities directly influence microclimates. As the urbanization expands, and the green spaces diminish, the heat islands begin to emerge. An old technique used during the past centuries—in both hot and dry climates of the central cities of Iran—was the moderation of microclimates via water and plants. With a diachronic approach to the study of the historical Chahar Bagh Street in Isfahan, this paper investigates the impact of the structural changes on its microclimate in three different scenarios, i.e., the street with its features during the Safavid Era (from 1501 to 1736); the street in its current status; and finally a probable critical condition resulting from complete elimination of natural elements from the environment. The mixed strategy used in this study relies on logical reasoning and software-assisted evaluation for comparing the three scenarios. The predicted mean vote (PMV) model was used for measuring thermal comfort. The results indicate that the evaluated comfort-providing area in the Safavid scenario is 7–17 times more favorable than the others. Moreover, the temperature in the contemporary era was found to be 1.5 degrees Celsius cooler than that of the critical status scenario.
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