A decision model based on decision tree and particle swarm optimization algorithms to identify optimal locations for solar power plants construction in Iran

Shorabeh, Saman Nadizadeh; Samany‬, Najmeh Neysani; Minaei, Foad; Firozjaei, Hamzeh Karimi; Homaee, Mehdi; Boloorani, Ali Darvishi

doi:10.1016/j.renene.2022.01.011

Cited by 57 publications

(23 citation statements)

References 32 publications

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“…• Customers with naive realism believe that their view of the world is valid and that others who disagree with them are irrational or dishonest [21].…”

Section: Traditional Media Fake Newsmentioning

confidence: 99%

Big Data ML-Based Fake News Detection Using Distributed Learning

Altheneyan

Alhadlaq

2023

IEEE Access

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Users rely heavily on social media to consume and share news, facilitating the mass dissemination of genuine and fake stories. The proliferation of misinformation on various social media platforms has serious consequences for society. The inability to differentiate between the sev-eral forms of false news on Twitter is a major obstacle to effective detection of fake news. Researchers have made progress toward a solution by placing a greater emphasis on methods for identifying bogus news. The dataset FNC-1, which includes four categories for identifying false news, will be used in this study. The state-of-the-art methods for spotting fake news are evaluated and compared using big data technology (Spark) and machine learning. The methodology of this study employed a decentralized Spark cluster to create a stacked ensemble model. Following feature extraction using N-grams, Hashing TF-IDF, and count vectorizer, we used the proposed stacked ensemble classification model. The results show that the suggested model has a superior classification performance of 92.45% in the F1 score compared to the 83.10 % F1 score of the baseline approach. The proposed model achieved an additional 9.35% F1 score compared to the state-of-the-art techniques.

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“…• Customers with naive realism believe that their view of the world is valid and that others who disagree with them are irrational or dishonest [21].…”

Section: Traditional Media Fake Newsmentioning

confidence: 99%

Big Data ML-Based Fake News Detection Using Distributed Learning

Altheneyan

Alhadlaq

2023

IEEE Access

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“…Being at home is necessary for COVID-19 patients. They should receive caregivers' services according to the disease level, age, affordance, and needed requirements [113][114][115]. Also, some medical services should be provided directly for them, which should consider their location, also the surrounding contexts.…”

Section: Context-aware Recommendation Systemsmentioning

confidence: 99%

Ten GIS-Based Solutions for Managing and Controlling COVID-19 Pandemic Outbreak

et al. 2022

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The coronavirus (COVID-19) pandemic has caused disastrous results in most countries of the world. It has rapidly spread across the globe with over 156 million cumulative confirmed cases and 3.264 million deaths to date, according to World Health Organization (WHO) Coronavirus Disease (COVID-19) Dashboard. With these huge amounts of causalities in the world, Geographic Information Systems (GIS) as a computer-based analyzer could help governments, experts, medical staff, and citizens to prevent and respond to the incidence. On the other hand, the COVID-19 pandemic involves many unknown parameters where most of them have a spatial dimension. Thus, spatial analysis and GIS could provide appropriate decisionmaking tools, predictive models, statistical methods, and new technologies for COVID-19 outbreak control, also help the people for avoiding direct contact and preserving social distance. This article aims to review the most promising categories of GIS-based solutions in this domain. We divided the solutions into ten classes including spatio-temporal analysis, SDSS approaches, geo-business, context-aware recommendation systems, participatory GIS and volunteered geographic information (VGI), internet of things (IoT), location-based service (LBS), web mapping, satellite imagery-based analysis, and waste management. The main contribution of this paper is proposing different geospatial guidelines that could provide reliable and useful protocols for COVID-19 outbreak control to minimize causalities, restrict incidence, establish effective urban communication, provide new approaches for business in lockdown situations, telehealth treatment, patient monitoring, adaptive decision making, and visualize trend analysis.

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“…On the one hand, GIS-technology provides a comprehensive tool for storing, manipulating, analysing, and representing geographical information, while MCDA yields a rich array of structural methods and algorithms for decision making, design, evaluation, and prioritization of options. The idea behind this integration is to extend GIS-based technology to give the user (decision maker) a degree of control over preferences (priorities of options) [23,24]. The main relevance of MCDA to spatial decision making is its capacity for identifying the most optimal solution and constructing a structured and creative approach to problem solving [25,26].…”

Section: Introductionmentioning

confidence: 99%

A Spatial Decision Support Approach for Flood Vulnerability Analysis in Urban Areas: A Case Study of Tehran

Afsari

Shorabeh

Kouhnavard

et al. 2022

IJGI

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Preparedness against floods in a hazard management perspective plays a major role in the pre-event phase. Hence, assessing urban vulnerability and resilience towards floods for different risk scenarios is a prerequisite for urban planners and decision makers. Therefore, the main objective of this study is to propose the design and implementation of a spatial decision support tool for mapping flood vulnerability in the metropolis of Tehran under different risk scenarios. Several factors reflecting topographical and hydrological characteristics, demographics, vegetation, land use, and urban features were considered, and their weights were determined using expert opinions and the fuzzy analytic hierarchy process (FAHP) method. Thereafter, a vulnerability map for different risk scenarios was prepared using the ordered weighted averaging (OWA) method. Based on our findings from the vulnerability analysis of the case study, it was concluded that in the optimistic scenario (ORness = 1), more than 36% of Tehran’s metropolis area was marked with very high vulnerability, and in the pessimistic scenario (ORness = 0), it was less than 1%was marked with very high vulnerability. The sensitivity analysis of our results confirmed that the validity of the model’s outcomes in different scenarios, i.e., high reliability of the model’s outcomes. The methodical approach, choice of data, and the presented results and discussions can be exploited by a wide range of stakeholders, e.g., urban planners, decision makers, and hydrologists, to better plan and build resilience against floods.

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A decision model based on decision tree and particle swarm optimization algorithms to identify optimal locations for solar power plants construction in Iran

Cited by 57 publications

References 32 publications

Big Data ML-Based Fake News Detection Using Distributed Learning

Big Data ML-Based Fake News Detection Using Distributed Learning

Ten GIS-Based Solutions for Managing and Controlling COVID-19 Pandemic Outbreak

A Spatial Decision Support Approach for Flood Vulnerability Analysis in Urban Areas: A Case Study of Tehran

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