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Prone Regions of Zoonotic Cutaneous Leishmaniasis in Southwest of Iran: Combination of Hierarchical Decision Model (AHP) and GIS
Background: Cutaneous leishmaniasis due to Leishmania major is an important public health problem in the world. Khuzestan Province is one of the main foci of zoonotic cutaneous leishmaniasis (ZCL) in the southwest of Iran. We aimed to predict the spatial distribution of the vector and reservoir(s) of ZCL using decision-making tool and to prepare risk map of the disease using integrative GIS, RS and AHP methods in endemic foci in Shush (plain area) and Khorramshahr (coastal area) counties of Khuzestan Province, southern Iran from Mar 2012 to Jan 2013. Methods: Thirteen criteria including temperature, relative humidity, rainfall, soil texture, soil organic matter, soil pH, soil moisture, altitude, land cover, land use, underground water depth, distance from river, slope and distance from human dwelling with the highest chance of the presence of the main vector and reservoir of the disease were chosen for this study. Weights of the criteria classes were determined using the Expert choice 11 software. The presence probability maps of the vector and reservoir of the disease were prepared with the combination of AHP method and Arc GIS 9.3. Results: Based on the maps derived from the AHP model, in Khorramshahr study area, the highest probability of ZCL is predicted in Gharb Karoon rural district. The presence probability of ZCL was high in Hossein Abad and Benmoala rural districts in the northeast of Shush. Conclusion: Prediction maps of ZCL distribution pattern provide valuable information which can guide policy makers and health authorities to be precise in making appropriate decisions before occurrence of a possible disease outbreak.
Journal Article
Crustal Stress Map of Iran: Insight From Seismic and Geodetic Computations
We used the focal mechanisms of crustal earthquakes (depth <40 km) in the period 1909–2012 and the available GPS velocities, derived from the data collected between 1999 to 2011, to estimate the magnitude and directions of maximum principal stress and strain rates in Iran. The Pearson product moment correlation was used to find the correlation between the stress field obtained from the focal mechanism stress inversion and that obtained using the seismic and geodetic strain rates. Our assumption is that stresses in a continuum are produced by tectonic forces and the consequent deformation on the crustal scale. Therefore, the direction of the stress and strain (or strain rate) are ideally to be the same. Our results show a strong correlation between the directions of the principal components of stress and strain (rate) obtained using the different data/methods. Using weighted average analysis, we present a new stress map for Iran.
Journal Article
Land Use and Land Cover Mapping Using Sentinel-2, Landsat-8 Satellite Images, and Google Earth Engine: A Comparison of Two Composition Methods
Accurate and real-time land use/land cover (LULC) maps are important to provide precise information for dynamic monitoring, planning, and management of the Earth. With the advent of cloud computing platforms, time series feature extraction techniques, and machine learning classifiers, new opportunities are arising in more accurate and large-scale LULC mapping. In this study, we aimed at finding out how two composition methods and spectral–temporal metrics extracted from satellite time series can affect the ability of a machine learning classifier to produce accurate LULC maps. We used the Google Earth Engine (GEE) cloud computing platform to create cloud-free Sentinel-2 (S-2) and Landsat-8 (L-8) time series over the Tehran Province (Iran) as of 2020. Two composition methods, namely, seasonal composites and percentiles metrics, were used to define four datasets based on satellite time series, vegetation indices, and topographic layers. The random forest classifier was used in LULC classification and for identifying the most important variables. Accuracy assessment results showed that the S-2 outperformed the L-8 spectral–temporal metrics at the overall and class level. Moreover, the comparison of composition methods indicated that seasonal composites outperformed percentile metrics in both S-2 and L-8 time series. At the class level, the improved performance of seasonal composites was related to their ability to provide better information about the phenological variation of different LULC classes. Finally, we conclude that this methodology can produce LULC maps based on cloud computing GEE in an accurate and fast way and can be used in large-scale LULC mapping.
Journal Article
Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms
Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms—Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine—in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naïve Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.
Journal Article
Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier
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.
Journal Article
Frontier fictions
InFrontier Fictions, Firoozeh Kashani-Sabet looks at the efforts of Iranians to defend, if not expand, their borders in the nineteenth and early twentieth centuries, and explores how their conceptions of national geography influenced cultural and political change. The \"frontier fictions,\" or the ways in which the Iranians viewed their often fluctuating borders and the conflicts surrounding them, played a dominant role in defining the nation. On these borderlands, new ideas of citizenship and nationality were unleashed, refining older ideas of ethnicity.
Kashani-Sabet maintains that land-based conceptions of countries existed before the advent of the modern nation-state. Her focus on geography enables her to explore and document fully a wide range of aspects of modern citizenship in Iran, including love of homeland, the hegemony of the Persian language, and widespread interest in archaeology, travel, and map-making. While many historians have focused on the concept of the \"imagined community\" in their explanations of the rise of nationalism, Kashani-Sabet is able to complement this perspective with a very tangible explanation of what connects people to a specific place. Her approach is intended to enrich our understanding not only of Iranian nationalism, but also of nationalism everywhere.
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