A Multiple Criteria Decision-Making Approach to Evaluate the Sustainability Indicators in the Villagers’ Lives in Iran with Emphasis on Earthquake Hazard: A Case Study

Murgante, Beniamino; Salmani, M; Qelichi, Mohamad Molaei; Hajilo, Mehdi

doi:10.3390/su9081491

Cited by 8 publications

(3 citation statements)

References 22 publications

(33 reference statements)

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“…Even if the households' motivations in creating new buildings for residential purposes was not the focus of this research, we may affirm that the progressive phenomena of depopulation and abandonment of historical settlements is a consequence of a social behavior oriented to use land properties for residential scope, more than for productive activities. The consequent urban expansion occurred with a pulverized growth model and this fragmented territorial system structure increased land take and has had a significant environmental impact [41][42][43][44][45].…”

Section: Discussionmentioning

confidence: 99%

Assessing Urban Fragmentation at Regional Scale Using Sprinkling Indexes

et al. 2018

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Artificial land use trends could represent an effective indicator of the settlement process quality and could also provide information about the efficacy of protection and exploitation policies in natural and rural areas. This work discusses an analytic procedure for the time series investigation of urban settlement development at the regional scale to verify the nexus between urban growth and demographic trends connected with the phenomenon of land take. In Italy, since 1950, the land take phenomenon has been a consequence of several factors: urbanization, realization of transport infrastructures including ports, airports, and highways, and the enhancement of industrial and productive systems. We analyzed all these territorial transformations that create waterproof soil, and more generally, a transition from natural and semi-natural uses toward artificial land use. After World War II, the demographic growth and the consequent housing demand generated a strong urbanization process in the main poles of economic development areas in Italy. Since the early 2000s, the situation has completely changed and the land take phenomenon is no longer mainly based on real need for new urban expansion areas based on effective urban planning tools, but is strongly related to a scattered demand for new housing in a weak territorial spatial planning system not able to drive effective urban development that minimizes speculative real estate initiatives. This uncontrolled occupation of soil generated, in Italy, a landscape fragmentation called the urban sprinkling phenomenon, different from urban sprawl, which is a wider phenomenon characterized by disordered urban growth. The present document aims to assess how uncontrolled expansion in areas characterized by low settlement density can generate fragmentation. To define if the territory is affected by the urban sprinkling phenomenon, two 50-year time series concerning urban expansion of buildings and demographic trends are analyzed calculating population and building density indices and their variation over the years. The sprinkling index is used to analyze the variation in the fragmentation degree at two different scales (regional and municipal). Finally, we discuss the context where this phenomenon has developed, analyzing the buildings located in hydrogeological risk zones and protected areas, and the correlation between demographic changes and the degree of territorial fragmentation variation.

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Section: Discussionmentioning

confidence: 99%

Assessing Urban Fragmentation at Regional Scale Using Sprinkling Indexes

et al. 2018

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“…A Multiple Criteria Decision-Making Approach has been proposed in [14]. The objective of the paper is to overcome the damage caused by earthquakes in rural areas.…”

Section: Related Workmentioning

confidence: 99%

A new model to improve decision-making indicators of bank customers' based on neural networks and particle swarm optimization algorithm

Asghari¹,

Azgomi

negahi³

2022

Preprint

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Due to the large volume of customers' banking information, classical algorithms face challenges in extracting knowledge and the decision-making process, so algorithms based on artificial intelligence and data mining can help this area. In this paper, we have presented a new model based on the artificial neural network and particle swarm optimization algorithm to improve bank customers' decision-making indicators. The proposed method is based on artificial neural networks and particle swarm optimization (PSO) algorithm. Due to the large volume of available data, the pure neural network can not achieve proper convergence in this area and suffers divergence or overfit. In this paper, the PSO algorithm is used to optimize neural network weights in extracting knowledge from user's banking data to improve the decision indicators. To evaluate the proposed method, two scenarios of optimized neural network using PSO and pure neural networks are used to make decisions between good and bad customers. Then, to evaluate the efficiency of the proposed method, the parameters of accuracy, Sensitivity, and Specificity for both scenarios were extracted and compared. The evaluations show that the accuracy of the optimized neural network is about 97%. In contrast, this value in the pure neural network is about 94%, indicating the proposed method's better performance. The proposed method also has better performance in the sensitivity parameter and feature selection and avoids overfitting than primary neural networks.

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“…The study area in this article is Sanandaj city, which located in N, 35 ° 18'52 "E 46 ° 59'32" E,in west of Iran and Kurdistan Province, with a population of 414069 and an area of 2906.04 km2,and due to the entity of two main faults, of this city it is at risk of earthquake. (Murgante B., 2017). Considering the topographical features of the city and its position in the mountainous region of Zagros with at an elevation of 1368 to 1357 m, Sanandaj can be categorized as a mountainous city (Ibrahim et al, 2015).…”

Section: Study Areamentioning

confidence: 99%

Exploitation of McDa to Learn the Radial Base Neural Network (Rbfnn) Aim Physical and Social Vulnerability Analysis Versus the Earthquake (Case Study: Sanandaj City, Iran)

Yariyan

Karami

Abbaspour

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Despite years of research on natural hazards and efforts to reduce physical and psychological damage, earthquake as a natural disaster is catastrophic. Though, human is the main axis in dealing with crisis and vulnerability, and since the space of cities encompasses largest population spectrum, managing this space is considered as an essential issue. Accordingly, the vulnerability of the City of Sanandaj was defined by environmental, physical and social criteria. In this regard, with the aim of modeling, and assessing the risk and vulnerability, the MCDA-ANN hybrid model was introduced as a new method for teaching of learning models. In order to determine the final value of each of the criteria, AHP analysis was performed as one of the MCDA methods to solve complex and non-structural problems by creating a functional hierarchy, and after that, a training data base for learning ANN was created randomly based on the AHP classification map. Then, for modeling, the radial base functional neural network (RBFNN) was used as one of the techniques of artificial neural networks. After the modeling, 30% of the points were selected as validation data to determine the accuracy of the model. After the implementation of RBFNN model, the area of AUC curve resulted is 0.922, which indicates the high accuracy of the model in assessing the risk of an earthquake. The results show high vulnerability in urban areas1 and 2 and in downtown Sanandaj that in these zones the physical and social factors dramatically affect the vulnerability of these areas.

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A Multiple Criteria Decision-Making Approach to Evaluate the Sustainability Indicators in the Villagers’ Lives in Iran with Emphasis on Earthquake Hazard: A Case Study

Cited by 8 publications

References 22 publications

Assessing Urban Fragmentation at Regional Scale Using Sprinkling Indexes

Assessing Urban Fragmentation at Regional Scale Using Sprinkling Indexes

A new model to improve decision-making indicators of bank customers' based on neural networks and particle swarm optimization algorithm

Exploitation of McDa to Learn the Radial Base Neural Network (Rbfnn) Aim Physical and Social Vulnerability Analysis Versus the Earthquake (Case Study: Sanandaj City, Iran)

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