Planning Tool Research Articles

A Novel Input Schematization Method for Coastal Flooding Early Warning Systems Incorporating Climate Change Impacts

Coastal flooding poses a significant threat to coastal communities, adversely affecting both safety and economic stability. This threat is exacerbated by factors such as sea level rise, rapid urbanization, and inadequate coastal infrastructure, as noted in recent climate change reports. Early warning systems (EWSs) have proven to be effective tools in coastal planning and management, offering a high cost-to-benefit ratio. Recent advancements have integrated operational numerical models with machine learning techniques to develop near-real-time EWSs, leveraging data obtained from reputable databases that provide reliable hourly sea-state and sea level data. Despite these advancements, a stepwise methodology for selecting representative events, akin to wave input reduction methods used in morphological modeling, remains undeveloped. Moreover, existing methodologies often overlook the significance of compound extreme events and their potential increased occurrence under climate change projections. This research addresses these gaps by introducing a novel input schematization method that combines efficient hydrodynamic modeling with clustering algorithms. The proposed methodοlogy, implemented in the coastal area of Pyrgos, Greece, aims to select an optimal number of representative sea-state and water level combinations to develop accurate EWSs for coastal flooding risk prediction. A key innovation of this methodology is the incorporation of weights in the clustering algorithm to ensure adequate representation of extreme compound events, also taking into account projections for future climate scenarios. This approach aims to enhance the accuracy and reliability of coastal flooding EWSs, ultimately improving the resilience of coastal communities against imminent flooding threats.

Transport Policy Pathways for Autonomous Road Vehicles to Promote Sustainable Urban Development in the European Union: A Multicriteria Analysis

The European Union’s policy aims for the wide-scale deployment of automated mobility by 2030, i.e., within the next programming period (2028–2034), with the deployment of autonomous road vehicles (AVs) in cities playing a key role. Researchers suggest that AV deployment will have complex impacts on urban development, which are difficult to quantify due to scarce real-life data. The present research aims to evaluate different policy pathways of AV deployment for sustainable urban development in the next EU programming period. A multicriteria analysis is conducted, combining AHP and VIKOR, with the participation of experts across Europe. Initially, the potential impacts on sustainable urban development are weighted as evaluation criteria. Then, different pathways are evaluated against these criteria, i.e., AV deployment as collective and/or private transport in specific areas and periods or in the whole Functional Urban Area (FUA) on a 24 h basis. An interesting finding is that the effect on the city’s spatial development, not thoroughly examined by literature, is highly ranked by experts. Regarding policy pathways, autonomous collective transport with 24 h service of the FUA emerged as the optimum alternative. The proposed methodology provides a tool for planners, researchers, and policy makers and a framework for an open debate with society.

Machine-learning based prediction for high health care utilizers using a multi-institution registry

Abstract Background Rising healthcare costs worldwide have spurred interest in using machine learning to pinpoint major healthcare utilizers for population health efforts. Previous studies have focused on individuals who impose the highest financial burden, such as targeting high-need high-cost utilizers, a small group with limited potential for cost reduction. Therefore, we developed models to predict future healthcare utilization across various thresholds. Methods We leveraged data from a multi-institutional diabetes database to develop binary classification models capable of predicting healthcare utilization in the following year. The models predicted six different outcomes: patients with an annual total length of stay of ≥ 7, ≥14, and ≥30 days, and emergency department (ED) attendance of ≥ 3, ≥5, and ≥10 visits. To mitigate class imbalance, we applied random and synthetic minority oversampling techniques and compared against models trained without oversampling. Models were trained with 2019 data and tested on unseen data from 2020 and 2021. Results Models trained with random oversampling, including logistic regression, multivariate adaptive regression splines, boosted trees, and multilayer perceptron, consistently demonstrated high AUC (>0.80) and sensitivity (>0.60) across both training-validation and test datasets. Addressing class imbalance was found to be crucial, as models without oversampling displayed satisfactory AUC (>0.80) but significantly lower sensitivity (<0.10). Key predictors identified included age, number of ED visits in the present year, chronic kidney disease stage, inpatient bed days in the present year, and mean HbA1c levels. Conclusions Our machine learning models successfully predict high service level utilization with strong performance, offering valuable tools for policymakers and health planners to develop targeted health initiatives based on patient utilization patterns. Key messages • Machine learning models can be leveraged to predict future healthcare utilization and facilitate interventional program development. • Annual total length of stay and total emergency department visits are useful service level indicators.

Transformation of Traditional Corporate Tax Planning into AI-Driven Corporate Tax Planning

Corporate tax planning has traditionally relied on manual processes and expert judgment to minimize tax liabilities. However, the rise of artificial intelligence (AI) presents new opportunities to transform these practices through automation, real-time analysis, and predictive capabilities. To conduct this research, a comprehensive review of academic literature, case studies, and existing AI tools in tax planning was carried out. The proposed AI-based tax planning model was developed based on these findings and validated through simulations and expert feedback. The study also examined the process of integrating AI systems with existing corporate tax structures to assess the feasibility and effectiveness of implementation. Results show that AI-driven corporate tax planning significantly enhances accuracy and efficiency compared to traditional methods. The AI model provides real-time analysis and predictive insights, enabling businesses to optimize tax strategies while ensuring compliance with evolving regulations. Tax authorities also benefit from AI implementation, gaining more transparent and equitable tax structures, along with stronger supervision capabilities. The proposed model can be developed into customized software tailored to individual companies, seamlessly integrating with existing corporate systems. These findings indicate that AI has the potential to revolutionize corporate tax planning by streamlining tax management and optimizing outcomes. For businesses, AI enables personalized tax strategies that adapt to specific data and regulations, improving both compliance and long-term tax optimization.

Green horizons: Exploring the potential of vertical green walls

The term “Green walls” also known as vertical gardens or living walls has emerged as a promising solution to combat the environmental challenges posed by rapid urbanization. This review explores the concept ornamental, plant choice suitable and benefits associated with green walls in urban settings. Utilising vegetation to cover vertical surfaces, green walls offer several benefits to the environment, society and economy. They contribute to improved air quality, noise reduction, energy conservation and biodiversity enhancement. Vertical greenery allows for the utilization of unused wall surfaces, creating green areas without occupying valuable ground space, which is often scarce in urban environments. Additionally, green walls offer aesthetic appeal, promote urban agriculture and create opportunities for community engagement. However, their implementation requires careful consideration of factors such as structural integrity, irrigation and maintenance. Despite these challenges, green walls offer a sustainable and innovative approach to enhancing the quality of urban life while mitigating the negative impacts of urban development on the environment. The current emphasis highlights the significance of green walls as an effective tool in sustainable urban planning, underscoring the need for further research and their integration into urban planning practices.

Development of micromodels for improvements in driving safety using as a case study a university campus in Panama

This research focuses on addressing critical driving safety issues on university campuses, particularly vehicular congestion, inadequate parking, and hazards arising from the interaction between vehicles and pedestrians. These challenges are common across campuses and demand effective solutions to ensure safe and efficient mobility. To address these issues, the study developed detailed microsimulation models tailored to the Victor Levi Sasso campus of the Technological University of Panama. The primary function of these models is to evaluate the effectiveness of various safety interventions, such as speed reducers and parking reorganization, by simulating their impact on traffic flow and accident risk. The models provide calculations of traffic parameters, including speed and travel time, under different safety scenarios, allowing for a comprehensive assessment of potential improvements. The results demonstrate that the proposed measures significantly enhance safety and traffic efficiency, proving the model’s effectiveness in optimizing campus mobility. Although the model is designed to tackle specific safety concerns, it also offers broader applicability for addressing general driving safety issues on university campuses. This versatility makes it a valuable tool for campus planners and administrators seeking to create safer and more efficient traffic environments. Future research could expand the model’s application to include a wider range of safety concerns, further enhancing its utility in promoting safer campus mobility.

Разноуровневые стратегии развития: арктический нефтегазовый вектор

The balanced development of the hydrocarbon potential in the Arctic is currently a strategic priority for Russia. Given the present conditions of the global energy sector, alongside new geopolitical challenges and trends towards low-carbon and ecologically balanced development, there is a need for a system of coordinated strategies across the industry, regional, and corporate levels. To effectively develop Arctic oil and gas resources, it is necessary to ensure technological sovereignty, which involves equipping oil and gas companies with domestically produced equipment and software. Strategies should also focus on the development of industries that produce high value-added products, particularly in the petrochemical sector. Public administrators and corporate managers responsible for key decisions must have a clear, long-term vision for the development of the Arctic oil and gas sector and employ established methodological approaches in strategy design. The goal of this study is to substantiate the specific characteristics of multilevel strategies and assess the feasibility of using a matrix approach when designing strategic alternatives relevant to the current economic environment. The research methodology is based on the approaches, methods, and tools of strategic management and planning, with a particular focus on methods for designing strategic alternatives. The novelty of the research lies in the development of a system of criteria for comparing strategies and their target priorities at the industry, regional, and corporate levels. The study identifies current trends and challenges arising from the energy transition, stringent sanctions policies, and growing technological capabilities. It proposes an approach to designing alternative strategies of related and unrelated diversification for oil and gas companies. Developing a holistic concept of coordinated industry and corporate strategies is identified as a promising area for further research.

Urban Perception Evaluation and Street Refinement Governance Supported by Street View Visual Elements Analysis

As street imagery and big data techniques evolve, opportunities for refined urban governance emerge. This study delves into effective methods for urban perception evaluation and street refinement governance by using street view data and deep learning. Employing DeepLabV3+ and VGGNet models, we analyzed street view images from Nanshan District, Shenzhen, identifying critical factors that shape residents’ spatial perceptions, such as urban greenery, road quality, and infrastructure. The findings indicate that robust vegetation, well-maintained roads, and well-designed buildings significantly enhance positive perceptions, whereas detractors like fences reduce quality. Furthermore, Moran’s I statistical analysis and multi-scale geographically weighted regression (MGWR) models highlight spatial heterogeneity and the clustering of perceptions, underscoring the need for location-specific planning. The study also points out that complex street networks in accessible areas enhance living convenience and environmental satisfaction. This research shows that integrating street view data with deep learning provides valuable tools for urban planners and policymakers, aiding in the development of more precise and effective urban governance strategies to foster more livable, resilient, and responsive urban environments.

Simulation and prediction of daytime surface urban heat island intensity under multiple scenarios via fully connected neural network

The intensification of the Surface Urban Heat Island (SUHI), driven by urbanization, land use and land cover (LULC) changes, and population growth, presents significant environmental and public health risks in urban areas. Simulating and predicting SUHI, particularly through the identification of future high SUHI intensity (SUHII) zones, has been recognized as a critical step in mitigating these effects. This study employs a Fully Convolutional Neural Network (FCNN) model, trained on data from four research sites, to simulate the current daytime SUHII across six validation sites in Singapore, utilizing 15 key independent variables identified in previous studies. The model exhibits high validation accuracy, achieving 87.45%. Three projection scenarios, based on projected population growth and LULC changes, predict a decrease in High SUHII across all validation sites, ranging from 98.3% to 9%. This reduction is attributed to the LULC improvements proposed in the 2019 Master Plan. Spatial analysis of the predicted SUHII maps indicates that the majority of High SUHII locations across scenarios remain consistent with the current situation. This research also suggests that the model could be a valuable tool for urban planners, allowing them to assess whether new urban development plans will effectively reduce High SUHII to desired thresholds, thereby mitigating SUHII in urban environments.

Business development management tools in the digital economy

The peculiarities of the digital environment, which contribute to the rapid growth of instability in enterprises' activities, are highlighted. The need to refine certain aspects and tools of traditional strategic planning is substantiated. The essence of digital transformation as a strategic process of business change—based on client-centric principles, systemic innovation, business model adaptation, active data usage, and competency development—is defined. The problems facing Ukrainian companies in digital transformation are highlighted, including insufficient strategy development, organizational culture misalignment with necessary changes, a shortage of digital skills, outdated technologies, lack of coordination between old and new technologies, and the high costs of implementing innovations. The approach to the digital transformation strategy is viewed as a tool for setting priorities that provides businesses with competitive advantages in rapidly changing markets through the application of digital technologies to solve new business challenges. Potential directions for ensuring the flexibility of a digital strategy are highlighted, such as developing partnerships and cooperation, utilizing big data analysis and predictive analytics for real-time decision-making, and employing flexible technologies for both implementation and strategy development. A hypothesis about the feasibility of developing a minimally viable strategy is proposed. As an additional method for increasing the flexibility of the digital transformation strategy, a modular structure for its design is proposed, based on the concept of business architecture, establishing a close connection between enterprise strategy, business model, and IT architecture. A brief overview of digital business models is provided, indicating that ecosystem-type business models, including digital platforms, are the most successful in the digital environment. The stages of digital transformation are outlined, and additional tools for their implementation are considered, such as digital maturity assessment models, a system of key indicators, and a digital transformation road map. A generalized structure of the toolkit for strategic management of business development in the digital environment is proposed, particularly aligned with the stages of digital transformation implementation.

Теоретичне підґрунтя публічного управління у сфері водопостачання та водовідведення

The sphere of water supply and drainage is a critically important component of the socio-economic mechanism, which is characterized by numerous challenges that were actualized in wartime. Aim of this paper is to analyze the main theoretical approaches of public management in the field of water supply and drainage in order to identify the main principles and tools that affect the efficiency and sustainability of the field. The tools of strategic planning, operational management and emergency response are considered as key components of successful management in this area. Emphasis is placed on the special role of developing fair tariff policies and financial support for infrastructure investments. The importance of the legal framework that regulates the sphere, ensuring compliance with environmental standards and protection of consumer rights is highlighted. The environmental aspect of resource management in the field, which includes modern methods of wastewater treatment and minimizing the negative impact on natural water bodies, is considered. It was emphasized that state intervention is a key factor in ensuring equal access to clean water, which is a basic human right according to the United Nations. The problem of natural monopoly in the field is considered through the prism of high capital investments and low elasticity of demand, which limit market competition. Strategies to stimulate competition and improve service quality are explored, including regulating tariffs and encouraging innovation. The example of Scotland, where water supply management is centralized through the creation of a single national company Scottish Water, reveals the effectiveness of such an approach in improving infrastructure and ensuring high quality services with public participation. The importance of a multidisciplinary approach to management in the field is emphasized, with a special emphasis on the stability of the service in wartime. The research may be useful to researchers, state and local authorities, and international organizations. Further perspectives of research include the study of the peculiarities of the application of the theoretical base in the solution of scientific and practical problems in the field in Ukraine during the war.

Digital Gamification Tool (Let's Control Flu) to Increase Vaccination Coverage Rates: Proposal for Algorithm Development.

Influenza represents a critical public health challenge, disproportionately affecting at-risk populations, including older adults and those with chronic conditions, often compounded by socioeconomic factors. Innovative strategies, such as gamification, are essential for augmenting risk communication and community engagement efforts to address this threat. This study aims to introduce the "Let's Control Flu" (LCF) tool, a gamified, interactive platform aimed at simulating the impact of various public health policies (PHPs) on influenza vaccination coverage rates and health outcomes. The tool aligns with the World Health Organization's goal of achieving a 75% influenza vaccination rate by 2030, facilitating strategic decision-making to enhance vaccination uptake. The LCF tool integrates a selection of 13 PHPs from an initial set proposed in another study, targeting specific population groups to evaluate 7 key health outcomes. A prioritization mechanism accounts for societal resistance and the synergistic effects of PHPs, projecting the potential policy impacts from 2022 to 2031. This methodology enables users to assess how PHPs could influence public health strategies within distinct target groups. The LCF project began in February 2021 and is scheduled to end in December 2024. The model creation phase and its application to the pilot country, Sweden, took place between May 2021 and May 2023, with subsequent application to other European countries. The pilot phase demonstrated the tool's potential, indicating a promising increase in the national influenza vaccination coverage rate, with uniform improvements across all targeted demographic groups. These initial findings highlight the tool's capacity to model the effects of PHPs on improving vaccination rates and mitigating the health impact of influenza. By incorporating gamification into the analysis of PHPs, the LCF tool offers an innovative and accessible approach to supporting health decision makers and patient advocacy groups. It enhances the comprehension of policy impacts, promoting more effective influenza prevention and control strategies. This paper underscores the critical need for adaptable and engaging tools in PHP planning and implementation. RR1-10.2196/55613.

Virtual Reality in planning, design, and management of urban green and blue infrastructure

There is a need for new tools in urban planning, design, and management (urban PDM) of green-blue infrastructure (GBI) to better support resilience and sustainable development. Virtual Reality (VR) emerges as a potential tool in this context. GBI provides a diversity of ecosystem services that increase the capacity of urban environments to absorb change while continuing to develop (adaptive capacity), a key factor in resilience and sustainability. However, there is a lack of tools capable of effectively capturing and communicating the designs, functions, and uses of GBI. Despite this need, research on the contributions of VR as a tool in urban PDM of GBI remains limited. To address this gap, an exploratory experiment using inductive qualitative data analysis was conducted. Participants of different ages and with different expertise viewed 360-degree video clips from urban areas through VR, completed a questionnaire, and participated in interviews. The results showed that the 360-degree video VR experience enhanced participants’ understanding of the viewed sites. Specifically, participants paid attention to details, captured the context, responded by generating emotional engagement, identified site development potential, and described an immersive experience. These results align with key principles for the sustainable management of ecosystem services, such as participation, learning, diversity, place-making, and stewardship; and adaptive capacity aspects such as processes and results grounded in complexity-embracing creativity. Our analysis shows that 360-degree video VR can increase the understanding of complexities, connections, and nuances in the urban environment. We conclude that 360-degree video VR could serve as a valuable complement to traditional tools in urban PDM, enriching decision-making with information that can support adaptive capacity, resilience, and sustainability.

Advances in CT-based lung function imaging for thoracic radiotherapy.

The objective of this review is to examine the potential benefits and challenges of CT-based lung function imaging in radiotherapy over recent decades. This includes reviewing background information, defining related concepts, classifying and reviewing existing studies, and proposing directions for further investigation. The lung function imaging techniques reviewed herein encompass CT-based methods, specifically utilizing phase-resolved four-dimensional CT (4D-CT) or end-inspiratory and end-expiratory CT scans, to delineate distinct functional regions within the lungs. These methods extract crucial functional parameters, including lung volume and ventilation distribution, pivotal for assessing and characterizing the functional capacity of the lungs. CT-based lung ventilation imaging offers numerous advantages, notably in the realm of thoracic radiotherapy. By utilizing routine CT scans, additional radiation exposure and financial burdens on patients can be avoided. This imaging technique also enables the identification of different functional areas of the lung, which is crucial for minimizing radiation exposure to healthy lung tissue and predicting and detecting lung injury during treatment. In conclusion, CT-based lung function imaging holds significant promise for improving the effectiveness and safety of thoracic radiotherapy. Nevertheless, challenges persist, necessitating further research to address limitations and optimize clinical utilization. Overall, this review highlights the importance of CT-based lung function imaging as a valuable tool in radiotherapy planning and lung injury monitoring.

Dynamic traffic network representation model for improving the prediction performance of passenger flow for mass rapid transit

Accurate machine learning predictions of passenger flow data for mass rapid transit (MRT) systems can considerably improve operational efficiency by enabling better allocation of train and human resources. However, such predictions are challenging because MRT networks have complex structures with route dependence and transfer stations. Although the static state of an MRT network has been computed in previous studies, a comprehensive understanding of an MRT network requires characterizing its dynamics. Therefore, this paper proposes a dynamic traffic network representation (DTNR) model that captures station features from historical traffic flows and geographical information of MRT stations. Furthermore, a multilevel attention network (MLAN) model is proposed to predict MRT passenger flow as a downstream task following the pretraining of the DTNR model. The experimental results of this study indicate that the developed DTNR and MLAN models can accurately predict MRT passenger flow. These models are widely applicable to different MRT systems and passenger flow situations, making them a valuable tool for transportation planners and operators.

An Age-Friendly Neighbourhood Index as a Long-Term Urban Planning Decision-Making Tool

People responsible for shaping the future of cities often seek valuable tools to assist in their decision-making processes. Using objective, quantified, and analysed data proves highly beneficial when determining where to focus interventions at the city level. Various urban indexes have been established to measure different aspects of urban life, ranging from sustainability to liveability. These indexes encompass multiple dimensions of a city, including mobility and walkability, among others. The age-friendly cities initiative developed indicators for assessing the age-friendliness of cities. Some researchers further refined these indicators to focus on urban planning competencies. Building on this foundation, this article aims to present an Age-Friendly Neighbourhood Index (AFNI) validated by a panel of experts using the Delphi method. This index can serve as a valuable tool for urban planners when they need to prioritise interventions to enhance age-friendliness at neighbourhood scale. The article also outlines the necessary data and measurement techniques for these indicators. The AFNI has been applied to a real case study in the city of Santander (Spain). This application assesses the age-friendliness of various neighbourhoods in Santander, demonstrating the challenges in acquiring sub-local quality data and emphasising the need for data-driven urban management.

Determination of the Change of Agricultural Landscapes Based on CORINE Land Cover Agricultural Land Classes Using GIS and Visual Quality Value with the AHP Method: The Case of Tekı̇rdağ Province

Agricultural landscapes contribute positively to the visual texture of the city and change over time. In this context, this study aims to determine the visual landscape quality of agricultural landscapes based on parameters on expert approach the hypothesis; it is important and necessary to determine the visual landscape values based on the change in agricultural landscapes over time. Within the framework of the developed hypothesis, it aims to make an expert-based visual landscape assessment of the agricultural landscapes of Tekirdağ Province, located in the northwestern part of Türkiye, based on nine subclasses created according to the CORINE land cover (CLC) classes, on the basis of 4 main parameters. Using the AHP technique, the priorities of the parameters and agricultural landscape classes and the relationships between basic parameters and visual preferences were determined. In this direction, the questions; What are the priorities of parameters that are effective in determining the visual quality of agricultural landscapes on the basis of expert approach?; What kind of changes have occurred in agricultural landscapes during the years 1990-2000-2006-2012-2018 in 9 subclasses created according to the CLC classes? and; How should the visual landscape quality values of the agricultural landscape subclasses created according to the CLC classes be ranked? The results of the study can be used as a tool in landscape planning and management studies as a factor in strengthening landscape quality.

UB-FineNet: Urban building fine-grained classification network for open-access satellite images

Fine classification of city-scale buildings using satellite imagery is a crucial research area with significant implications for urban planning, infrastructure development, and population distribution analysis. However, the task faces great challenges due to low-resolution overhead images acquired from high-altitude space-borne platforms and the long-tailed sample distribution of fine-grained urban building categories, leading to a severe class imbalance problem. To address these issues, we propose a deep network approach to the fine-grained classification of urban buildings using open-access satellite images. A Denoising Diffusion Probabilistic Model (DDPM) based super-resolution method is first introduced to enhance the spatial resolution of satellite images, which benefits from domain-adaptive knowledge distillation. Then, a new fine-grained classification network with Category Information Balancing Module (CIBM) and Contrastive Supervision (CS) technique is proposed to mitigate the problem of class imbalance and improve the classification robustness and accuracy. Experiments on Hong Kong data set with 11 distinct building types revealed promising classification results with a mean Top-1 accuracy of 60.45%, which is on par with street-view image based approaches. A comprehensive ablation study demonstrates that the CIBM and CS modules improve Top-1 accuracy by 2.6% and 3.5%, respectively, over the baseline approach. In addition, these modules can be easily integrated into other classification networks, achieving similar performance improvements. This research advances urban analysis by providing an effective solution for detailed classification of buildings in complex mega-city environments using only open-access satellite imagery. The proposed technique can serve as a valuable tool for urban planners, aiding in the understanding of economic, industrial, and population distribution within cities and regions, ultimately facilitating informed decision-making in urban development and infrastructure planning. Data and code will be publicly available at https://github.com/ZhiyiHe1997/UB-FineNet.

From Urban Design to Energy Sustainability: How Urban Morphology Influences Photovoltaic System Performance

In response to the pressing need for sustainable urban development amidst global population growth and increased energy demands, this study explores the impact of an urban block morphology on the efficiency of building photovoltaic (PV) systems amidst the pressing global need for sustainable urban development. Specifically, the research quantitatively evaluates how building distribution and orientation influence building energy consumption and photovoltaic power generation through a comprehensive simulation model approach, employing tools, such as LightGBM, for the enhanced predictability and optimization of urban forms. Our simulations reveal that certain urban forms significantly enhance solar energy utilization and reduce cooling energy requirements. Notably, an optimal facade orientation and building density are critical for maximizing solar potential and overall energy efficiency. This study introduces novel findings on the potential of machine learning techniques to predict and refine urban morphological impacts on solar energy efficacy, offering robust tools for urban planners and architects. We discuss how strategic urban and architectural planning can significantly contribute to sustainable energy practices, emphasizing the application of our results in diverse climatic contexts. Future research should focus on refining these simulation models for broader climatic variability and integrating more granular urban morphology data to enhance precision in energy predictions.

Evaluating effectiveness of clustering algorithms in multiple target stereotactic radiosurgery

Objective. Single-isocenter-multiple-target technique for stereotactic radiosurgery (SRS) can reduce treatment duration but risks compromised dose coverage due to potential rotational errors. Clustering targets into two groups can reduce isocenter-target distances, mitigating the impact of rotational uncertainty. However, a comprehensive evaluation of clustering algorithms for SRS is absent. This study addresses this gap by introducing the SRS Target Clustering Framework (Framework), a comprehensive tool that utilizes commonly used clustering algorithms to generate efficient cluster configurations. Approach. The Framework incorporates four distinct optimization objectives based on two key metrics: the isocenter-target distance and the ratio of this distance to the target radius. Agglomerative and weighted agglomerative clustering are employed for minimax and weighted minimax objectives, respectively. K-means and weighted k-means are utilized for sum-of-squares and weighted sum-of-squares objectives. We applied the Framework to 126 SRS plans, comparing results to ground truth solutions obtained through a brute force algorithm. Main results. For the minimax objective, the average maximum isocenter-target distance from agglomerative clustering (4.8 cm) was slightly higher than the ground truth (4.6 cm). Similarly, the weighted agglomerative clustering achieved an average maximum ratio of 15.1 compared to the ground truth of 14.6. Notably, both k-means and weighted k-means clustering showed close agreement (within a precision of 0.1) with the ground truth for average root-mean-square target-isocenter distance and ratio (3.6 cm and 11.1, respectively). Significance. These results demonstrate the Framework’s effectiveness in generating clusters for SRS targets. The proposed approach has the potential to become a valuable tool in SRS treatment planning. Furthermore, this study is the first to investigate clustering algorithms for both minimizing maximum and sum-of-squares uncertainty in SRS.