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Al-Shaikhi A, Nuha HH, Lawal A, Rehman S, Mohandes M. Vertical Wind Profile Estimation Using Hybrid Convolutional Neural Networks and Bidirectional Long Short-Term Memory. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2023. [DOI: 10.1007/s13369-023-07665-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
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Earthquake Vulnerability Assessment for Urban Areas Using an ANN and Hybrid SWOT-QSPM Model. REMOTE SENSING 2021. [DOI: 10.3390/rs13224519] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Tabriz city in NW Iran is a seismic-prone province with recurring devastating earthquakes that have resulted in heavy casualties and damages. This research developed a new computational framework to investigate four main dimensions of vulnerability (environmental, social, economic and physical). An Artificial Neural Network (ANN) Model and a SWOT-Quantitative Strategic Planning Matrix (QSPM) were applied. Firstly, a literature review was performed to explore indicators with significant impact on aforementioned dimensions of vulnerability to earthquakes. Next, the twenty identified indicators were analyzed in ArcGIS, a geographic information system (GIS) software, to map earthquake vulnerability. After classification and reclassification of the layers, standardized maps were presented as input to a Multilayer Perceptron (MLP) and Self-Organizing Map (SOM) neural network. The resulting Earthquake Vulnerability Maps (EVMs) showed five categories of vulnerability ranging from very high, to high, moderate, low and very low. Accordingly, out of the nine municipality zones in Tabriz city, Zone one was rated as the most vulnerable to earthquakes while Zone seven was rated as the least vulnerable. Vulnerability to earthquakes of residential buildings was also identified. To validate the results data were compared between a Multilayer Perceptron (MLP) and a Self-Organizing Map (SOM). The scatter plots showed strong correlations between the vulnerability ratings of the different zones achieved by the SOM and MLP. Finally, the hybrid SWOT-QSPM paradigm was proposed to identify and evaluate strategies for hazard mitigation of the most vulnerable zone. For hazard mitigation in this zone we recommend to diligently account for environmental phenomena in designing and locating of sites. The findings are useful for decision makers and government authorities to reconsider current natural disaster management strategies.
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Adedeji PA, Akinlabi SA, Madushele N, Olatunji OO. Hybrid neurofuzzy investigation of short-term variability of wind resource in site suitability analysis: a case study in South Africa. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06001-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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A Heuristic Approach to Siting and Design Optimization of an Onshore Wind Farm Layout. ENERGIES 2020. [DOI: 10.3390/en13225946] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The forecasted electricity demand in Saudi Arabia may be around 120 GW/year by 2032. As per the latest government announcement, Saudi Arabia is aiming to install 57.5 GW of renewable energy capacity by 2030. In this study, firstly, a wind map is developed based on the historical wind data, recorded over a 39-year period, followed by the development of the geographic information system (GIS)-based multi-criteria decision making (MCDM) model for suitable wind farm site selection for Hijaz, the western region of Saudi Arabia. This region is selected as it has a population density of around 25 per sq. km, the highest in Saudi Arabia. For the model, data from various ecological, environmental, and socioeconomic criteria are considered. Finally, the optimization of the wind farm layout on the identified suitable region of 5.5 km × 4 km is performed using the deep-array wake model, DAWM. The optimized layout has locations for 30 wind turbines of 3 MW rated capacity. This optimization process minimizes energy losses and costs and maximizes power production. The net and gross energy production from the wind farm are expected to be 143 GWh and 156 GWh, respectively, with an array loss of 8.25% at a cost of energy of USD 65.66 per MWh, and a capacity factor of 17.7%. The cost calculations include the capital cost of constructing the access roads and a complete collector system with two substations. The optimized turbine positions in the layout have a major and minor axis separation of 1680 m and 448 m, respectively.
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Prediction of surface tension of the binary mixtures containing ionic liquid using heuristic approaches; an input parameters investigation. J Mol Liq 2020. [DOI: 10.1016/j.molliq.2019.111976] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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Wang Y, Yu Y, Cao S, Zhang X, Gao S. A review of applications of artificial intelligent algorithms in wind farms. Artif Intell Rev 2019. [DOI: 10.1007/s10462-019-09768-7] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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A Hybrid Analytic Network Process and Artificial Neural Network (ANP-ANN) Model for Urban Earthquake Vulnerability Assessment. REMOTE SENSING 2018. [DOI: 10.3390/rs10060975] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Yao L, Li T, Li Y, Long W, Yi J. An improved feed-forward neural network based on UKF and strong tracking filtering to establish energy consumption model for aluminum electrolysis process. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3357-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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