No abstract

In the very early hours of 26th December 2003, a devastating and strong earthquake with a magnitude of 6.5 struck Bam, one of the historical cities of Kerman province in the south of Iran. According to the official reports, more than 30,000 were killed and about 25,000 injured. More than 80% of the town's buildings were also destroyed. After the disaster, Bam's reconstruction management process was presented with a lot of challenges and faced many fundamental questions. The number of human losses and related social issues, extensive destruction of the historical town, and also the lack of good experience in the reconstruction of a city or town made the reconstruction project of Bam more complicated. The reconstruction of Bam was the most important postdisaster reconstruction project among recent reconstructions in Iran. Many factors, such as concern over the government and international agencies, the new managerial approaches, and the application of appropriate reconstruction methods, made it different from the other reconstruction programs. Thus, the post-earthquake reconstruction of Bam is investigated in this research with respect to the importance of this issue. The aim behind this article is to give a brief explanation of the earthquake reconstruction management policies in Bam and also the plans for the reconstruction and rebuilding of urban residential and commercial units.

The prediction of municipal solid waste generation (MSWG) plays an important role in a solid waste management system. However, achieving the anticipated prediction accuracy with regard to the nonhomogeneous nature of waste and effect of various and out of control factors on MSWG is quite challenging. In this article, support vector machine (SVM), one of the artificial intelligence techniques, and hybrid of wavelet transform (WT) and support vector machine (WT‐SVM) are used to predict weekly time series of MSWG in Tehran and Mashhad cites during the period of January 2006–December 2011. To improve the performance of SVM model, considering the influence of noise and the disadvantages of traditional noise eliminating technologies, the wavelet denoising method is applied to reduce or eliminate the noise in MSWG time series. Since Data‐driven models such as SVM involve potential of uncertainty that is difficult to quantify, uncertainty determination is one of important gaps observed in SVM results analysis. Therefore, Monte Carlo method was used to analyze uncertainty of the model results. Results showed both models could precisely predict MSWG in Tehran and Mashhad cites. However, the preprocessing of input variables by WT led to develop a more accurate model for prediction of weekly MSWG in both cities. The uncertainty analysis also verified that the WT‐SVM model had more robustness than SVM and had a lower sensitivity to change of input variables. © 2013 American Institute of Chemical Engineers Environ Prog, 33: 220–228, 2014

Decisions on selecting an appropriate site for temporary shelter used to be taken in a limited amount of time after a disaster. The need for a systematic method in this area inspired the MADM (multi-attribute decision making) for complex disaster management decisions. This research proposes a model for appropriate and systematic site selection for temporary shelters, before an earthquake, using a geographical information system and MADM based on an earthquake damage assessment. After the effective criteria for site selection of temporary shelters are determined, the geographical layers of these criteria are prepared for Municipal District No.1 of Greater Tehran, the capital of Iran. Given these attributes and the required shelter area (415-610 hectares), 14 zones are proposed initially. Various MADM methods are used for the final selection. The mean of the aggregated ranking results are determined, and 10 of the 14 initial zones are ranked.

Different models have been proposed for disaster management by researchers and agencies. Despite their efficiency in some locations, disasters are still a fundamental challenge in the way of sustainable development. The purpose of this research is developing a comprehensive conceptual model for disaster management using thematic analysis. In this regard, first, disaster management models are collected. In the next stage, the themes of each model are extracted and categorised in three phases. In the first phase that is descriptive coding, available elements in each model are extracted as code and the basic themes are recognised. Then, in the phase of interpretive coding, basic themes are classified in three categories that are called organising themes (i.e. hazard assessment, risk management and management actions). In the final phase, strategic management is selected as the global or overarching theme to integrate all the other themes. Based on thematic analysis, it can be concluded that disaster management has three main elements that are the three organising themes. Therefore, comprehensive model of disaster management should include these three elements and their sub-basic themes that is called the ideal or criterion type. Results showed that some scientists have looked at disaster management one dimensionally (one theme). Even in two-dimensional models, one dimension has advantage over the other one. While the proposed typology in this study showed that the comprehensive model should include all the three mentioned elements.