The spatial analysis of thunderstorm hazard in Iran

In this study, monthly, quarterly, and annual frequency data of thunderstorm days of 25 long-term synoptic weather stations during the period from 1960 to 2010 were analyzed applying Ward’s Hierarchical Cluster Analysis (WHCA) Method and Kriging Geostatistical Method (KGM). The results of temporal analysis of Thunderstorm Days (TD) in Iran showed that in terms of frequency, seasonal occurrence of this phenomenon is mostly in transitional seasons of spring and autumn. The results of WHCA to find homogeneous places in terms of synchronization and timing of TD reflects the fact that there are five clusters with similar memberships, including the North West, West, the southern part, northern, central, and northeastern parts, eastern regions, and center of Iran, and in this classification, the frequency of occurrence of TD reduces in the same order the regions are mentioned. In contrast, the lowest frequency of TD is in summer and winter seasons. In this study, it was found that among various deterministic and geostatistical methods, KGM is the most suitable one for thunderstorms hazard zonation and for classifying the different regions based on thunderstorm occurrence; WHCA is more suitable than other methods. The results of spatial analysis of thunderstorms point to the fact that the core of the mentioned thunderstorms is mostly in mountainous areas, particularly, highlands of North West and West of Iran. With regard to place, in the West part of the country, especially North West (Tabriz, Oroomieh, and Zanjan stations) and West, thunderstorms have higher frequencies, while the South East, South, Central, and eastern regions are less affected by the thunderstorm hazard.     

Performance analysis of radial basis function networks and multi-layer perceptron networks in modeling urban change: a case study

The majority of cities are rapidly growing. This makes the monitoring and modeling of urban change’s spatial patterns critical to urban planners, decision makers, and environment protection activists. Although a wide range of methods exists for modeling and simulating urban growth, machine learning (ML) techniques have received less attention despite their potential for producing highly accurate predictions of future urban extents. The aim of this study is to investigate two ML techniques, namely radial basis function network (RBFN) and multi-layer perceptron (MLP) networks, for modeling urban change. By predicting urban change for 2010, the models’ performance is evaluated by comparing results with a reference map and by using a set of pertinent statistical measures, such as average spatial distance deviation and figure of merit. The application of these techniques employs the case study area of Mumbai, India. The results show that both models, which were tested using the same explanatory variables, produced promising results in terms of predicting the size and extent of future urban areas. Although a close match between RBFN and MLP is observed, RBFN demonstrates higher spatial accuracy of prediction. Accordingly, RBFN was utilized to simulate urban change for 2020 and 2030. Overall, the study provides evidence that RBFN is a robust and efficient ML technique and can therefore be recommended for land use change modeling.     

Evidence for Accelerating Glacier Ice Loss in the Takht＇ e Solaiman Mountains of Iran from 1955 to 2010

This study reports on the clean ice area and surface elevation changes of the Khersan and Merjikesh glaciers in the north of Iran between 1955 and 2010 based on several high to medium spatial resolution remote sensing data.The object-oriented classification technique has been applied to nine remote sensing images to estimate the debris-free areas.The satellite-based analysis revealed that the clean ice areas of Khersan and Merjikesh glaciers shrank since 2010 with an overall area decrease of about 45% and 60% respectively.It means that the dramatic proportions of 1955 glaciers surface area are covered with debris during the last five decades.Although the general trend is a clean ice area decrease,some advancement is observed over the period of 1997-2004.During 1987-1991 the maximum decrease in the clean ice area was observed.However,the clean ice area had steadily increased between 1997 and 2010.To quantify the elevation changes besides the debris-free change analysis,several Digital Elevation Models(DEMs) were extracted from aerial photo(1955),topographic map(1997),ASTER image(2002) and Worldview-2 image(2010) and after it a 3-D Coregistration and a linear relationship adjustments techniques were used to remove the systematic shifts and elevation dependent biases.Unlike the sinusoidal variation of our case studies which was inferred from planimetric analysis,the elevation change results revealed that the glacier surface lowering has occurred during 1955-2010 continuously without any thickening with the mean annual thinning of about 0.4 ± 0.04 m per year and 0.3 ± 0.026 m per year for Khersan and Merjikesh glaciers,respectively.The maximum thinning rate has been observed during 1997-2002(about 1.1 ± 0.09 per year and 0.96 ± 0.01 mper year,respectively),which was compatible partially with debris-free change analysis.The present result demonstrates that although in debris-covered glaciers clean ice area change analysis can illustrate the direction of changes(retreat or advance),due to the high uncertainty in glacier area delineation in such glaciers,it cannot reveal the actual glacier changes.Thus,both planimetric and volumetric change analyses are very critical to obtain accurate glacier variation results.