scholarly journals Corrigendum: Landfill site selection in Makkah using geographic information system and analytical hierarchy process

2019 ◽  
pp. 0734242X1989624
2019 ◽  
Vol 38 (3) ◽  
pp. 245-253 ◽  
Author(s):  
Faisal A Osra ◽  
George W Kajjumba

Municipal solid waste is a problem to developed and developing cities in the world. If municipal solid waste is not managed well, it can be a source of numerous contaminants to water, air, and soil. Although landfill is at the bottom in terms of priorities of municipal solid waste management techniques, its applicability cannot be neglected in developing economies. Landfill site selection is a hard puzzle comprised of political, social, economic, and environmental factors. Makkah, Saudi Arabia, is targeting 30 million pilgrims by 2030, putting the city in a difficult circumstance: More pilgrims, more municipal solid waste. The current dump site, Kakia, is expected to be full by 2020; thus, there is a need to locate a new landfill site. In 20 years, Makkah is expected to produce 44 million tonnes of municipal solid waste, which requires approximately a 7.5 m × 5,874,000 square meter landfill capacity. In this study, a geographic information system, analytical hierarchy process, vertical electrical sounding, and ground-penetrating radar are applied to select the best new landfill site for Makkah. By combining these techniques, there are three suitable site locations: (39°36 ́38.45 ́ ́E: 21°18 ́26.46 ́ ́N), (39°37 ́54.07 ́ ́E: 21°19 ́35.25 ́ ́N), and (39°44 ́04.45 ́ ́E: 21°13 ́08.93 ́ ́N). These sites have a considerable depth to water table of 12 m. Therefore, the city of Makkah should use these findings to establish a sanitary landfill.


Salmand ◽  
2018 ◽  
Vol 12 (4) ◽  
pp. 506-517
Author(s):  
Omid Jamshidi ◽  
Morteza Doostipasha ◽  
Seyed Mohamad Hosein Razavi ◽  
Mahmood Gudarzi ◽  
◽  
...  

2020 ◽  
Vol 13 (3) ◽  
pp. 1145
Author(s):  
Fabiano Peixoto Freiman ◽  
Camila De Oliveira Carvalho

A identificação de áreas suscetíveis a inundações é essencial para o gerenciamento de desastres e definição de políticas públicas. O objetivo deste trabalho é a apresentação de um método para identificação de áreas suscetíveis a inundações através da integração de informações geográficas provenientes de técnicas do Sensoriamento Remoto, as ferramentas do Sistema de Informação Geográfica (SIG), a lógica Fuzzy e a aplicação de Métodos de Análise Multicritério (MAM) Analytical Hierarchy Process (AHP). Para atingir o objetivo foi proposto um estudo de caso, localizado na Bacia do Rio Bengalas, nos municípios de Nova Friburgo e Bom Jardim (Região Serrana do Rio de Janeiro). A modelagem espacial multicritério foi realizada a partir da seleção de um conjunto de dados composto por informações geomorfológicas, hidrológicas e de uso e ocupação do solo. Como resultado, obteve-se um mapa de suscetibilidade a inundações para a região. A coerência do modelo gerado foi verificada a partir do histórico de inundações da bacia do Rio Bengalas. A metodologia, apresentou-se eficiente e adequada para a determinação de áreas suscetíveis a inundações, prevendo com sucesso a distribuição espacial de áreas com riscos a inundações.  Spatial modelling of flood-susceptible areas based on a hybrid multi-criteria model and Geographic Information System: a case study applied to the Bengalas River basin A B S T R A C TThe identification of areas susceptible to flooding is essential for disaster management and public policy making. The objective of this work is the presentation of a method for the identification of areas susceptible to floods through the integration of geographic information from Remote Sensing techniques, Geographic Information System (GIS) tools, Fuzzy logic and the application of Multicriteria Analysis Methods (MAM) Analytical Hierarchy Process (AHP). In order to achieve the objective, a case study was proposed, located in the Bengalas River Basin, in the municipalities of Nova Friburgo and Bom Jardim (Mountain Region of Rio de Janeiro). Multicriteria spatial modeling was performed by selecting a data set composed of geomorphological, hydrological and land use information. As a result, a flood susceptibility map was obtained for the region. The coherence of the generated model was verified from the flood history of the Bengalas River basin. The methodology was efficient and adequate for the determination of areas susceptible to floods, successfully predicting the spatial distribution of areas at risk of flooding.Keywords: flood susceptibility. Fuzzy logic. MAM. AHP. GIS. 


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