Identifying Exposure of Urban Area to Certain Seismic Hazard Using Machine Learning and GIS: A Case Study of Greater Cairo

Hamdy, Omar; Gaber, Hanan; Abdalzaher, Mohamed S.; El-Hadidy, Mahmoud

doi:10.3390/su141710722

Cited by 41 publications

(10 citation statements)

References 86 publications

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“…Data commonly employed in spatial interpolation are elevation data, rainfall data, meteorological data, topography, and population density (Sukkuea & Heednacram, 2022). This method effectively predicts the geographic data distribution, increasing data density, designating a fierce distribution of data with a small data set coverage, and acquiring complete information of unmeasured data (Hamdy et al, 2022).…”

Section: Methodsmentioning

confidence: 99%

A Comparison of Different Gis-Based Interpolation Methods for Bathymetric Data: Case Study of Bawean Island, East Java

Pratomo,

Safira,

Stefani

2023

Geodesy and Cartography

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The bottom surface’s portrayal is crucial in many different practices. Therefore, accurate bathymetry data is required. The interpolation method is one element that influences the accuracy of a Single Beam Echosounder’s depth data. IDW, Kriging, and TIN are three standard interpolation techniques. This study compares these three methods with two scenarios utilizing the spatial analysis to establish the most effective technique for producing the digital elevation model of the seafloor beneath Bawean Island. The IDW exhibits the strongest R-squared (0.9998779 in Scenario-1 and 0.9999875 in Scenario-2) and correlation (0.9998796 in Scenario-1 and 0.9999595 in Scenario-2). It indicates that IDW and bathymetric data have the closest relationships. IDW has the lowest error, as measured by the MAE value (0.02 in Scenario-1 and 0.009 in Scenario-2), followed in both cases by Kriging and TIN. Additionally, the RMSE for IDW shows the same outcome (0.045 in Scenario 1 and 0.016 in Scenario 2). In the meantime, comparing the first and second scenarios reveals that the second, which has fewer data, is preferable to the first. Since the MAE and RMSE in the first scenario are greater than those in the second, we may infer that more data leads to more significant errors.

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Section: Methodsmentioning

confidence: 99%

A Comparison of Different Gis-Based Interpolation Methods for Bathymetric Data: Case Study of Bawean Island, East Java

Pratomo,

Safira,

Stefani

2023

Geodesy and Cartography

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“…mr l ,w = 0. It is clear from (13) that each relay performs RE harvesting if it is selected or not. This energy amount supports the relay's future transmission and motivates it for participation.…”

Section: B Relay Energy Harvesting Modelmentioning

confidence: 99%

An Optimal Balanced Energy Harvesting Algorithm for Maximizing Two-Way Relaying D2D Communication Data Rate

et al. 2022

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Combining energy harvesting (EH) and device-to-device (D2D) communication underlaying 5G cellular networks is a very promising direction to improve both energy and spectral efficiencies. Unlike conventional relay-aided D2D communication that assumes one-way relaying (OWR) protocols, this paper proposes a two-way relaying (TWR) model. It aims to maximize the TWR D2D link rate that shares the uplink (UL) resources of the conventional cellular user (CU) considering the quality of service (QoS) constraints of all users. Besides, the relays are considered to harvest renewable energy (RE) from the ambient environment by relying on an attached solar panel. Also, they can harvest radio frequency (RF) energy from the received signal based on the power splitting (PS) EH protocol. Assuming that the UL resource allocation (RA) is already performed, the paper's objective is to jointly optimize the transmission power of all users in addition to the PS factor of relays based on the well-known meta-heuristic algorithm particle swarm optimization (PSO). Also, the best relay is selected by relying on the delimited area (DA) mechanism and the balanced residual energy (BRE) leading to TWR D2D link rate maximization and better energy efficiency (EE). The performance of the proposed algorithm is investigated through the results as well as comparing its performance to two of the most recent relay-aided D2D algorithms.INDEX TERMS Device-to-device, two-way relaying, energy harvesting, power splitting, particle swarm optimization, power allocation, decode-and-forward, relay selection.

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“…Machine learning (ML) is a crucial tool for enabling AI [23] as it can effectively predict and schedule network resources based on the available data inputs [24,25]. It has applications in various areas providing data acquisition and analysis by emulating human learning behavior of knowledge [26].…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Based Traffic Classification in Software-Defined Networks

Serag,

Abdalzaher,

Elsayed

et al. 2024

Electronics

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Many research efforts have gone into upgrading antiquated communication network infrastructures with better ones to support contemporary services and applications. Smart networks can adapt to new technologies and traffic trends on their own. Software-defined networking (SDN) separates the control plane from the data plane and runs programs in one place, changing network management. New technologies like SDN and machine learning (ML) could improve network performance and QoS. This paper presents a comprehensive research study on integrating SDN with ML to improve network performance and quality-of-service (QoS). The study primarily investigates ML classification methods, highlighting their significance in the context of traffic classification (TC). Additionally, traditional methods are discussed to clarify the ML outperformance observed throughout our investigation, underscoring the superiority of ML algorithms in SDN TC. The study describes how labeled traffic data can be used to train ML models for appropriately classifying SDN TC flows. It examines the pros and downsides of dynamic and adaptive TC using ML algorithms. The research also examines how ML may improve SDN security. It explores using ML for anomaly detection, intrusion detection, and attack mitigation in SDN networks, stressing the proactive threat-detection and response benefits. Finally, we discuss the SDN-ML QoS integration problems and research gaps. Furthermore, scalability and performance issues in large-scale SDN implementations are identified as potential issues and areas for additional research.

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Identifying Exposure of Urban Area to Certain Seismic Hazard Using Machine Learning and GIS: A Case Study of Greater Cairo

Cited by 41 publications

References 86 publications

A Comparison of Different Gis-Based Interpolation Methods for Bathymetric Data: Case Study of Bawean Island, East Java

A Comparison of Different Gis-Based Interpolation Methods for Bathymetric Data: Case Study of Bawean Island, East Java

An Optimal Balanced Energy Harvesting Algorithm for Maximizing Two-Way Relaying D2D Communication Data Rate

Machine-Learning-Based Traffic Classification in Software-Defined Networks

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