Disaster management base on geoinformatics

Shamshiry, Elmira; Nadi, Behzad; Mokhtar, Mazlin; Komoo, Ibrahim; Hashim, Habibah

doi:10.1109/iccsn.2011.6014842

Cited by 7 publications

(4 citation statements)

References 11 publications

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“…Several AI software platforms and libraries have been used to solve MSW management problems. MATLAB was used in the training and testing of neural networks and MLP algorithms [29][30][31][32][33][34][35]. SPSS was used to correlate model attributes such as population density and waste generation [17,[36][37][38].…”

Section: Methodsmentioning

confidence: 99%

Comparative Analysis of the Implementation of Support Vector Machines and Long Short-Term Memory Artificial Neural Networks in Municipal Solid Waste Management Models in Megacities

Meza

Yepes

Rodrigo-Ilarri

et al. 2023

IJERPH

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The development of methodologies to support decision-making in municipal solid waste (MSW) management processes is of great interest for municipal administrations. Artificial intelligence (AI) techniques provide multiple tools for designing algorithms to objectively analyze data while creating highly precise models. Support vector machines and neuronal networks are formed by AI applications offering optimization solutions at different managing stages. In this paper, an implementation and comparison of the results obtained by two AI methods on a solid waste management problem is shown. Support vector machine (SVM) and long short-term memory (LSTM) network techniques have been used. The implementation of LSTM took into account different configurations, temporal filtering and annual calculations of solid waste collection periods. Results show that the SVM method properly fits selected data and yields consistent regression curves, even with very limited training data, leading to more accurate results than those obtained by the LSTM method.

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Section: Methodsmentioning

confidence: 99%

Comparative Analysis of the Implementation of Support Vector Machines and Long Short-Term Memory Artificial Neural Networks in Municipal Solid Waste Management Models in Megacities

Meza

Yepes

Rodrigo-Ilarri

et al. 2023

IJERPH

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“…Where: S= suitability value (0 or 1); r j = suitability index value for each constraint criterion (0 or 1) and m= total number of constraint criteria. Thus, this formula multiplied all the above described criteria to propose a landfill site by excluding all those areas which were unsuitable for landfill site (Shamshiry, et al, 2011).…”

Section: Methodsmentioning

confidence: 99%

Application of Gis Based Model in Landfill Site Selection: A Case Study of Lahore, Pakistan

Rathore

2023

PJS

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Open dumping sites for solid wastes in Asian developing countries, including Pakistan, have recently received particular attention in terms of environmental pollution problems. In the Lahore District, Mehmood Booti is the only authorized open dumping site developed by city district government which is now unable to fulfill district’s requirement. Therefore the data was collected to propose a new landfill site in Lahore district, Pakistan. These data sets involved the processing of spatial information that was utilized in GIS environment using Boolean Operation as a tool for land suitability analysis. In order to achieve desired results, five input raster layers; including water bodies, roads, important places, existing dumping site and tube well were prepared. After omission of unsuitable areas, the suitability analysis of the residue areas was accomplished.

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“…For countries that have vulnerability to natural disasters, the use of geospatial technology is an important part of natural disaster management, planning and mitigation [1] [7]. One way to reduce disaster risk is to use information technology [8].…”

Section: Introductionmentioning

confidence: 99%

Implementation of Mobile Data Collection System for Disaster Rapid Emergency Response System Using Open Data Kit

Ichwana

Juhana

2018

2018 International Conference on Information Technology Systems and Innovation (ICITSI)

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Indonesia is a disaster-prone country. One of the main problems related to disaster in Indonesia is that the country does not yet have a good disaster management that can function optimally in responding to disaster. Therefore, this research is aimed to produce a system that can provide emergency response for disaster management using mobile data collection system. This system would provide crucial and immediate information needed by the decision makers to decide emergency response and recovery procedure. This is a research and development research. The data assessment process is carried out by the assessor at the disaster location using ODK collect. The data then is saved in a mobile communication device. Data can be uploaded by the disaster server network when the connectivity is available. Assessment data can be viewed on the application server through a web admin. The system was generated by considering the law of the state of Indonesia no. 24 Year 2007 concerning disaster management agency planning and regulation by Head of BNPB (Indonesia National Board for Disaster Management) No. 9 Year 2008 on the standard procedure for emergency response team of national board for disaster. Data over disaster occurrence, its massive impact, emergency facilities or supplies needed, post-disaster condition, and a missing victim is provided in these queries. After being tested, it appears that the design and implementation of an emergency disaster response system have been able to run satisfactorily according to system specifications.

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Disaster management base on geoinformatics

Cited by 7 publications

References 11 publications

Comparative Analysis of the Implementation of Support Vector Machines and Long Short-Term Memory Artificial Neural Networks in Municipal Solid Waste Management Models in Megacities

Comparative Analysis of the Implementation of Support Vector Machines and Long Short-Term Memory Artificial Neural Networks in Municipal Solid Waste Management Models in Megacities

Application of Gis Based Model in Landfill Site Selection: A Case Study of Lahore, Pakistan

Implementation of Mobile Data Collection System for Disaster Rapid Emergency Response System Using Open Data Kit

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