Artificial neural networks for the generation of a conductivity map of the ground

Kalogirou, Soteris A.; Florides, Georgios A.; Pouloupatis, Panayiotis; Christodoulides, Paul; Joseph-Stylianou, Josephina

doi:10.1016/j.renene.2014.12.033

Cited by 19 publications

(4 citation statements)

References 26 publications

(27 reference statements)

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“…The extensive applications of artificial neural networks in various studies include, but not limited to, the prediction of food pathogen Escherichia coli (Gosukonda et al , 2015), generation of a contour map of the ground conductivity (Kalogirou et al , 2015), river water quality modeling (Sarkar and Pandey, 2015), prediction of gas storage capacities in metal organic frameworks (Yildiz and Uzun, 2015), thermal analysis of heat exchangers (Mohanraj et al , 2015) and flood forecasting (Elsafi, 2014).…”

Section: Methodsmentioning

confidence: 99%

Vulnerability assessment of urban community and critical infrastructures for integrated flood risk management and climate adaptation strategies

Espada

Apan

McDougall

2017

IJDRBE

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Purpose The purpose of this paper was to develop an integrated framework for assessing the flood risk and climate adaptation capacity of an urban area and its critical infrastructures to help address flood risk management issues and identify climate adaptation strategies. Design/methodology/approach Using the January 2011 flood in the core suburbs of Brisbane City, Queensland, Australia, various spatial analytical tools (i.e. digital elevation modeling and urban morphological characterization with 3D analysis, spatial analysis with fuzzy logic, proximity analysis, line statistics, quadrat analysis, collect events analysis, spatial autocorrelation techniques with global Moran’s I and local Moran’s I, inverse distance weight method, and hot spot analysis) were implemented to transform and standardize hazard, vulnerability, and exposure indicating variables. The issue on the sufficiency of indicating variables was addressed using the topological cluster analysis of a two-dimension self-organizing neural network (SONN) structured with 100 neurons and trained by 200 epochs. Furthermore, the suitability of flood risk modeling was addressed by aggregating the indicating variables with weighted overlay and modified fuzzy gamma overlay operations using the Bayesian joint conditional probability weights. Variable weights were assigned to address the limitations of normative (equal weights) and deductive (expert judgment) approaches. Applying geographic information system (GIS) and appropriate equations, the flood risk and climate adaptation capacity indices of the study area were calculated and corresponding maps were generated. Findings The analyses showed that on the average, 36 (approximately 813 ha) and 14 per cent (approximately 316 ha) of the study area were exposed to very high flood risk and low adaptation capacity, respectively. In total, 93 per cent of the study area revealed negative adaptation capacity metrics (i.e. minimum of −23 to <0), which implies that the socio-economic resources in the area are not enough to increase climate resilience of the urban community (i.e. Brisbane City) and its critical infrastructures. Research limitations/implications While the framework in this study was obtained through a robust approach, the following are the research limitations and recommended for further examination: analyzing and incorporating the impacts of economic growth; population growth; technological advancement; climate and environmental disturbances; and climate change; and applying the framework in assessing the risks to natural environments such as in agricultural areas, forest protection and production areas, biodiversity conservation areas, natural heritage sites, watersheds or river basins, parks and recreation areas, coastal regions, etc. Practical implications This study provides a tool for high level analyses and identifies adaptation strategies to enable urban communities and critical infrastructure industries to better prepare and mitigate future flood events. The disaster risk reduction measures and climate adaptation strategies to increase urban community and critical infrastructure resilience were identified in this study. These include mitigation on areas of low flood risk or very high climate adaptation capacity; mitigation to preparedness on areas of moderate flood risk and high climate adaptation capacity; mitigation to response on areas of high flood risk and moderate climate adaptation capacity; and mitigation to recovery on areas of very high flood risk and low climate adaptation capacity. The implications of integrating disaster risk reduction and climate adaptation strategies were further examined. Originality/value The newly developed spatially explicit analytical technique, identified in this study as the Flood Risk-Adaptation Capacity Index-Adaptation Strategies (FRACIAS) Linkage/Integrated Model, allows the integration of flood risk and climate adaptation assessments which had been treated separately in the past. By applying the FRACIAS linkage/integrated model in the context of flood risk and climate adaptation capacity assessments, the authors established a framework for enhancing measures and adaptation strategies to increase urban community and critical infrastructure resilience to flood risk and climate-related events.

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Section: Methodsmentioning

confidence: 99%

Vulnerability assessment of urban community and critical infrastructures for integrated flood risk management and climate adaptation strategies

Espada

Apan

McDougall

2017

IJDRBE

View full text Add to dashboard Cite

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“…Kalogirou et al [214] implemented an ANN, similar to a previous study [212], for the estimation of ground thermal conductivity in Cyprus. The estimated information was then used to generate geothermal maps for conductivity for the first 100 m in dry soil.…”

Section: Thermal Conductivitymentioning

confidence: 99%

Estimation of renewable energy and built environment-related variables using neural networks – A review

Rodrigues

Gomes

Gaspar

et al. 2018

Renewable and Sustainable Energy Reviews

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This paper presents a review on the application of neural networks for the estimation, forecasting, monitoring, and classification of exogenous environmental variables that affect the performance, salubrity, and security of cities, buildings, and infrastructures. The forecast of these variables allows to explore renewable energy and water resources, to prevent potentially hazardous construction locations, and to find the healthiest places, thus promoting a more sustainable future. Five research themes are covered-solar, atmospheric, hydrologic, geologic, and climate change. The solar section comprises solar radiation, direct and diffuse radiation, infrared and ultraviolet radiation, clearness index, and sky luminance and luminous efficacy. The atmospheric section reviews wind, temperature, humidity, cloud classification, and storm prediction. The hydrologic section focuses on precipitation, rainfall-runoff, hail, snow, drought, flood, tides, water levels, and other variables. The geologic section covers works on landslides, earthquakes, liquefaction, erosion, soil classification, soil mechanics, and other properties. Finally, climate change forecasting and downscaling of climate models are reviewed. This work demonstrates the wide range of applications of these methods in different research fields. Some research gaps and interdisciplinary research opportunities are identified for future development of comprehensive forecast and evaluation approaches regarding the estimation of renewable energy and built environment-related variables.

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“…Since early 2000, the trend of using hybrid methodologies has emerged in the literature in which more than one models is combined to achieve better forecasts of wind speed in future and spatial domains [26][27][28]. These modern machine learning methods are very useful and provide relatively better estimates both in time and spatial domains as can be seen from wide ranging applications like perfor-mance prediction of thermosiphon solar water heaters [29], analysis of absorption systems [30], sizing of pho-tovoltaic systems [31,32] ground conductivity map generation [33], and solar radiation forecasting [34].…”

Section: Introductionmentioning

confidence: 99%

Accuracy of wind speed predictability with heights using Recurrent Neural networks

Mohandes¹,

Rehman²,

Nuha³

et al. 2021

FME Transactions

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Accurate prediction of wind speed in future time domain is critical for wind power integration into the grid. Wind speed is usually measured at lower heights while the hub heights of modern wind turbines are much higher in the range of 80-120m. This study attempts to better understand the predictability of wind speed with height. To achieve this, wind data was collected using Laser Illuminated Detection and Ranging (LiDAR) system at 20m, 40m, 50m, 60m, 80m, 100m, 120m, 140m, 160m, and 180m heights. This hourly averaged data is used for training and testing a Recurrent Neural Network (RNN) for the prediction of wind speed for each of the future 12 hours, using 48 previous values. Detailed analyses of short-term wind speed prediction at different heights and future hours show that wind speed is predicted more accurately at higher heights.For example, the mean absolute percent error decreases from 0.19 to 0.16as the height increase from 20m to 180m, respectively for the 12 th future hour prediction. The performance of the proposed method is compared with Multilayer Perceptron (MLP) method. Results show that RNN performed better than MLP for most of the cases presented here at the future 6th hour.

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Artificial neural networks for the generation of a conductivity map of the ground

Cited by 19 publications

References 26 publications

Vulnerability assessment of urban community and critical infrastructures for integrated flood risk management and climate adaptation strategies

Vulnerability assessment of urban community and critical infrastructures for integrated flood risk management and climate adaptation strategies

Estimation of renewable energy and built environment-related variables using neural networks – A review

Accuracy of wind speed predictability with heights using Recurrent Neural networks

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