Multi-scale offshore wind farm site selection decision framework based on GIS, MCDM and meta-heuristic algorithm

The objective of this study is the development and implementation of a geographic information system (GIS)-based methodology to determine suitable sites for wind farms by using multicriteria decision making (MCDM) techniques. Many eligibility criteria are considered in this approach, such as GIS analysis is performed in combination with MCDM techniques, both for the criteria weighting process (using the analytical hierarchical process) and for the standardization/classification of the criteria values into a common scale, in order to finally apply the weighted linear combination technique. The MCDM output is a suitability map, which provides the spatial distribution of the final score, showing zones ranging from non-suitable locations to highly suitable areas after categorizing them into five classes. Furthermore, three scenarios regarding the criteria selection and their hierarchy are investigated: technical, techno-economic, and techno-economic-environmental. For each of these scenarios, different criteria are assigned different levels of importance, and the corresponding results are compared and evaluated. The proposed approach is presented through a case study for the regional unit of Chania (Crete Island, Aegean Sea, Greece), for which the necessary geospatial data were collected, created, and processed. This analysis highlights the capabilities of GIS in performing site selection for onshore wind farms while considering the regulations, legislations, and other constraints.

Introduction: The electricity generated from nuclear plants and petroleum-based products has a negative influence on the environment as a whole. It has shown the utility to search out and promote the utilization of renewable, environmentally friendly, and sustainable energy sources such as solar, wind, and geothermal. Nowadays, Wind energy resource has quickly emerged as the world’s fastest-growing energy source.Methods: However, the selection of the most suitable places for developing a wind farm is a crucial challenge that can be seen as a problem of site selection, which involves numerous conflicting variables. Therefore, it is classified as an MCDM (multi-criteria decision-making) problem. The main objective of this research is to determine the best locations in Burundi for the installation of wind farms. The Fuzzy Analytic Hierarchy Process (FAHP) was used to weigh the criteria considering their relative importance. This study considers several key factors when determining the optimal location for a wind farm. These factors include wind speed, slope, proximity to the grid network, distance to roads, and land use/land cover (LULC). Furthermore, a geographic information system (GIS) is utilized to generate the final suitability wind farm locations map.Results and Discussion: The obtained results indicate that 20.91% of the whole study area is suitable nevertheless, only 1.96% is tremendously suitable for wind turbine placement. The western part of Burundi is the optimal area for constructing a wind farm, and the most is in Lake Tanganyika.

This study aims to show a c GIS-based site-selection with multi-criteria decision analysis for offshore wind farms while taking into account energy and exergy efficiency analyses of the selected model wind turbine. The analytic hierarchy process (AHP) was used to identify potential sites dependent on several critical physical parameters. The first phase of the study is the data collection, which involves the construction of a geographic information system (GIS). The second phase is the exclusion step. In the final stage, energy and exergy efficiencies of the wind energy generating system have been investigated. Meteorological parameters are examined on exergy and energy efficiency, and results are shown. The most significant result achieved from this study is that Bozcada has potential sites for installing offshore wind turbines by means of meteorological parameters and suitable location.

Roles of geospatial technology in eco-industrial park site selection: State–of–the-art review

Density-Based Spatial Clustering of Application with Noise (DBSCAN) is a well-known, unsupervised machine learning tool for clustering. DBSCAN creates clusters based on dense regions while marking points that lie alone in low-density regions as outliers. In DBSCAN, the density is detected via core points which are quite sensitive to input parameters: ϵ is radius of the neighborhood and MinPts is minimum number of points constraint within ϵ radius. In contrast to crisp core point definition, intuitionistic fuzzy core point definition makes DBSCAN algorithm capable to detect different patterns of density by two different combinations of input parameters. In this study, a DBSCAN extension is proposed based on this idea: IFDBSCAN. The proposed extension is then illustrated by a computational experiment on a synthetic dataset. Results show that, IFDBSCAN can establish fine-tuned clusters relatively to classical DBSCAN and provide users more insight on the estimation of input parameters.

In recent years an increasing number of countries have implemented policy measures to promote renewable energy. However, the most important problem that the policy makers face with is the conflicting linguistic terms and subjective opinions on energy and environment policy. As the environmental policy and energy policy always go hand in hand, it is quite clear that wind as a renewable resource should be competitive with conventional power generation sources. From technical, environmental, socio-economical and socio-political standpoint, wind power is the most deserving of all of the cleaner energy production options (geothermal, solar, tidal, biomass, hydro) for more widespread deployment. Although wind power is a never ending green resource, assessment of environmental risks and impactswhich comprise the backbone of environmental policyin the context of specific projects or sites often are necessary to explicate and weigh the environmental trade-offs that are involved. In the case of wind farms, a number of turbines (ranging from about 250 kW to 750 kW) are connected together to generate large amounts of power. Apart from the constraints resulting from the number of turbines, any site selection should think over the technical, economic, social, environmental and political aspects. Each aspect uses criteria for its own evaluation. Decision making by using multi criteria decision analysis is an attractive solution for obtaining an integrated decision making result. Although Lee et al. (2009), Kaya and Kahraman (2010) and Tegou et al. (2010) has studied wind farm site selection by using different kinds of Analytic Hierarchy Process (AHP), Cheng’s extent analysis of Fuzzy AHP (FAHP) is used in this study and a holistic hierarchy were developed. The analytic hierarchy process (AHP) is a multi-criteria decision making tool to deal with complex, unstructured and multi-attribute problems. This method is distinguished from other multi-criteria methods in three ways: I. Construction of the hierarchy structure II. Pairwise comparisons of different criteria III. Weighing with respective to the overall objective. In AHP, decision makers quantify the importance of criteria by using Cheng’s 1-9 scale. To overcome the disadvantage of reluctant and inconsistent comparison judgments, fuzzy analytic hierarchy process (FAHP) might be used on each factor to determine the weight of fuzziness of its attributes. Hierarchy structure diagram of wind farm site selection is given in Figure 1. This study aims to apply the FAHP to find priority sequence of alternatives and obtain the key success factors for the selection of appropriate sites of wind farms.

PurposeRecent investigations failed to reproduce the positive rotor-guided ablation outcomes shown by initial studies for treating persistent atrial fibrillation (persAF). Phase singularity (PS) is an important feature for AF driver detection, but algorithms for automated PS identification differ. We aim to investigate the performance of four different techniques for automated PS detection.Methods2048-channel virtual electrogram (VEGM) and electrocardiogram signals were collected for 30 s from 10 patients undergoing persAF ablation. QRST-subtraction was performed and VEGMs were processed using sinusoidal wavelet reconstruction. The phase was obtained using Hilbert transform. PSs were detected using four algorithms: (1) 2D image processing based and neighbor-indexing algorithm; (2) 3D neighbor-indexing algorithm; (3) 2D kernel convolutional algorithm estimating topological charge; (4) topological charge estimation on 3D mesh. PS annotations were compared using the structural similarity index (SSIM) and Pearson’s correlation coefficient (CORR). Optimized parameters to improve detection accuracy were found for all four algorithms using Fβ score and 10-fold cross-validation compared with manual annotation. Local clustering with density-based spatial clustering of applications with noise (DBSCAN) was proposed to improve algorithms 3 and 4.ResultsThe PS density maps created by each algorithm with default parameters were poorly correlated. Phase gradient threshold and search radius (or kernels) were shown to affect PS detections. The processing times for the algorithms were significantly different (p < 0.0001). The Fβ scores for algorithms 1, 2, 3, 3 + DBSCAN, 4 and 4 + DBSCAN were 0.547, 0.645, 0.742, 0.828, 0.656, and 0.831. Algorithm 4 + DBSCAN achieved the best classification performance with acceptable processing time (2.0 ± 0.3 s).ConclusionAF driver identification is dependent on the PS detection algorithms and their parameters, which could explain some of the inconsistencies in rotor-guided ablation outcomes in different studies. For 3D triangulated meshes, algorithm 4 + DBSCAN with optimal parameters was the best solution for real-time, automated PS detection due to accuracy and speed. Similarly, algorithm 3 + DBSCAN with optimal parameters is preferred for uniform 2D meshes. Such algorithms – and parameters – should be preferred in future clinical studies for identifying AF drivers and minimizing methodological heterogeneities. This would facilitate comparisons in rotor-guided ablation outcomes in future works.

Background: This study focuses on the latest knowledge regarding Micro, Small and Medium Enterprises (MSMEs) as a current central issue. These enterprises have shown their significance in providing employment opportunities and contributing to the country’s economy. However, MSMEs face various challenges that must be addressed to optimize their outcomes. Understanding the characteristics of this group was crucial in formulating effective strategies. Objective: This study proposed to cluster or combine micro, small, and medium enterprises (MSMEs) data in a particular area based on asset value and turnover. As a result, this study aimed to gain insights into the MSME landscape in the area and provided valuable information for decision-makers and stakeholders. Methods: This study utilized two methods, namely the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) method and the K-Means method. These methods were chosen for their distinct capabilities. DBSCAN was selected for its ability to handle noisy data and identify clusters with diverse forms, while K-Means was chosen for its popularity and ability to group data based on proximity. The study used a dataset containing MSME information, including asset values and turnover, collected from various sources. Results: The outcomes encompassed identifying clusters of MSMEs based on their closeness in the feature space within a specific region. Optimizing the clustering outcomes involved modifying algorithm parameters like epsilon and minimum points for DBSCAN and the number of clusters for K-Means. Furthermore, this study attained a deeper understanding of the arrangement and characteristics of MSME clusters in the region through a comparative analysis of the two methodologies. Conclusion: This study offered perspectives on clustering MSMEs based on asset value and turnover in a specific region. Employing DBSCAN and K-Means methodologies allowed researchers to depict the MSME landscape and grasp the business attributes of these enterprises. These results could aid in decision-making and strategic planning concerning the advancement of the MSME sector in the mentioned area. Future study may investigate supplementary factors and variables to deepen comprehension of MSME clusters and promote regional growth and sustainability. Keywords: Asset Value, Clustering, DBSCAN, K-Means, Turnover

Environmental benefits mining based on data-driven taxi cruising recommendation strategy

The density based notion for clustering approach is used widely due to its easy implementation and ability to detect arbitrary shaped clusters in the presence of noisy data points without requiring prior knowledge of the number of clusters to be identified. Density-based spatial clustering of applications with noise (DBSCAN) is the first algorithm proposed in the literature that uses density based notion for cluster detection. Since most of the real data set, today contains feature space of adjacent nested clusters, clearly DBSCAN is not suitable to detect variable adjacent density clusters due to the use of global density parameter neighborhood radius Nrad and minimum number of points in neighborhood Npts. So the efficiency of DBSCAN depends on these initial parameter settings, for DBSCAN to work properly, the neighborhood radius must be less than the distance between two clusters otherwise algorithm merges two clusters and detects them as a single cluster. Through this paper: 1) We have proposed improved version of DBSCAN algorithm to detect clusters of varying density adjacent clusters by using the concept of neighborhood difference and using the notion of density based approach without introducing much additional computational complexity to original DBSCAN algorithm. 2) We validated our experimental results using one of our authors recently proposed space density indexing (SDI) internal cluster measure to demonstrate the quality of proposed clustering method. Also our experimental results suggested that proposed method is effective in detecting variable density adjacent nested clusters.

A method of two-stage clustering learning based on improved DBSCAN and density peak algorithm

An efficient and scalable density-based clustering algorithm for datasets with complex structures

This study aims to objectively analyze the feasibility of prospective recipients of the Smart Indonesia Card Scholarship (KIP-K) by integrating the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The research dataset consists of 287 data on prospective scholarship recipients with 11 main attributes that reflect the socio-economic and academic conditions of students. The research process includes data collection, pre-processing, transformation of categorical attributes into numerical values using a linear weighting scheme, cluster analysis using DBSCAN, and candidate ranking using TOPSIS. DBSCAN is used to identify cluster patterns and detect anomalies in the data of potential recipients, while TOPSIS is used to rank candidates based on proximity to the ideal solution. The results of the grouping produced 10 clusters and one noise cluster that showed a variety of socio-economic characteristics of prospective scholarship recipients. The results of the ranking show that some of the candidates with the highest TOPSIS scores come from clusters with higher levels of economic vulnerability. In addition, some of the high-scoring candidates also came from the noise cluster, indicating that even though they did not belong to a particular group, they still met the eligibility criteria based on a multi-criteria evaluation. These findings show that the combination of DBSCAN and TOPSIS has the potential to support the process of analyzing the eligibility of scholarship recipients in a more systematic and data-driven manner.

Image segmentation is the process to divide a digital image into a number of regions for further usage. Color images can be segmented by applying density-based clustering methods, e.g. Density-Based Spatial Clustering of Applications with Noise (DBSCAN), which is used to identify the arbitrary shaped clusters. The drawback of DBSCAN is the high computational complexity whilst the size of image input is normally very large. Self-Organizing Map (SOM) is a dimensionality reduction method which can be applied to reduce the dimensions of image processing tasks. This paper proposes a hybrid method of SOM and DBSCAN (SOM-DBSCAN) for image segmentation. To evaluating the usability of the proposed SOM-DBSCAN method, four images are used to benchmark image segmentation.

Rapid urbanization to meet the needs of the growing population has led to several challenges such as pollution, increased and congested traffic, poor sustainability, and impact on the ecological environment. The conception of smart cities comprising intelligent convergence systems has been regarded as a potential solution to overcome these problems. Based on the information, communications, and technology (ICT), the idea of a smart city has emerged to decrease the impact of rapid urbanization. In this context, important efforts have been made for making cities smarter and more sustainable. However, the challenges associated with the implementation and evaluation of smart cities in developing countries are not examined appropriately, particularly in the Moroccan context. To analyze the efficacy and success of such efforts, the evaluation and comparisons using common frameworks are significantly important. For this purpose, the present research aims to investigate and evaluate the most influential dimensions and criteria for smart city development (SCD) in the Moroccan context. To reach this goal, this study proposes a new integrated Multi-Criteria Decision-Making (MCDM) model based on Intuitionistic Fuzzy Analytical Hierarchy Process (IF-AHP) and Intuitionistic Fuzzy Decision-Making Trial and Evaluation Laboratory (IF-DEMATEL). In the given context, the IF-AHP is employed to analyze the structure of the problem and calculate the weights of the qualitative and quantitative dimensions/criteria by incorporating the uncertainty values provided by the experts. Later, IF-DEMATEL is used to construct the structural correlation of dimensions/criteria in MCDM. The use of intuitionistic fuzzy set theory helps in dealing with the linguistic imprecision and the ambiguity of experts’ judgment. Results reveal that ‘Smart Living and Governance’ and ‘Smart Economy’ are major dimensions impacting the SCD in the Moroccan context. The proposed model focuses on enhancing the understanding of different dimensions/criteria and situations in smart cities compared to traditional cities and elevates their decision-making capability. Moreover, the results are discussed, as are the managerial implications, conclusions, limitations, and potential opportunities.