Planning Decisions Research Articles

Urban Spatial Heat Resilience Indicator Based on Running Activity Z-Score

The assessment of urban heat resilience has become crucial due to increasing extreme weather events. This study introduces the Running Activity Z-score (RAZ) index based on running activity trajectory data to evaluate heat resilience. Through a case study of an August 2022 heatwave in Beijing, we examined the index’s sensitivity to extreme heat and explored its spatial relationships with key built environment factors, including plot ratio, green coverage, population density, and blue space proximity. Our results reveal two key findings: (1) the RAZ index serves as an effective real-time, high-precision indicator of urban heatwave impacts, as evidenced by extremely low RAZ values consistently coinciding with heatwave periods, and (2) the RAZ index offers valuable insights for identifying potential low heat resilience areas and supporting planning decisions, as demonstrated by its significant correlations with built environment factors that align with previous studies while uncovering more detailed spatial relationships. Although RAZ effectively complements traditional measurement methods, its application requires careful consideration of external factors such as social dynamics and climate variability.

Attitudes, Time Pressure, and Behavior Change Techniques Affect Route Journey Planning Decisions: Evidence from an RCT

Transport emissions are a major contributor to global CO2 emissions, requiring interventions to promote sustainable travel behaviors. This study examines how behavior change techniques (BCTs), attitudinal and behavioral segmentation, and time pressure influence green route selection in a simulated journey-planning app. Using a randomized 2 × 3 × 3 factorial design, 600 UK participants completed travel booking tasks under three time-pressure scenarios (low, moderate, high) using either a control app or a BCT-enhanced intervention app. Participants were segmented based on environmental attitudes, public transport preferences, and travel needs. Multilevel logistic regression showed significant main effects for condition, segment, and time pressure. Participants using the intervention app were more likely to select green routes (5.39, p < 0.001). Segments with a more positive attitude to public transport demonstrated higher baseline green route selection compared to those with low public transport attitudes (odds ratio [OR] = 0.31, p = 0.020). Moderate time pressure facilitated the highest likelihood of green route selection, while low (OR = 0.16, p < 0.001) and high (OR = 0.48, p < 0.001) time pressures reduced green bookings. Interaction effects were non-significant, potentially reflecting the sample size. The findings highlight the potential of BCT-enhanced apps to promote sustainable travel, particularly when tailored to user segments and designed to address time pressure. Future research should explore real-world applications and intervention durability.

Amplified summer wind stilling and land warming compound energy risks in Northern Midlatitudes

Abstract Wind energy plays a critical role in mitigating climate change and meeting growing energy demands. However, the long-term impacts of anthropogenic warming on wind resources, particularly their seasonal variations and potential compounding risks, remain understudied. Here we analyze large-ensemble climate simulations in high-emission scenarios to assess the projected changes in near-surface wind speed and their broader implications. Our analyses show robust wind changes including a decrease of wind speed (i.e., stilling) up to ~15% during the summer months in Northern Midlatitudes. This stilling is linked to amplified warming of the midlatitude land and the overlying troposphere. Despite regional and model uncertainties, robust signals of warming-induced wind stilling will likely emerge from natural climate variations in the late 21st century under the high-emission scenarios. Importantly, the summertime wind stilling coincides with a projected surge in cooling demand, and their compounding may disrupt the energy supply-demand balance earlier. These findings highlight the importance of considering the seasonal responses of wind resources and the associated climate-energy risks in a warming climate. By integrating these insights into future energy planning decisions, we can better adapt to a changing climate and ensure a reliable and resilient energy future.

Integrating machine learning for treatment decisions in anterior open bite orthodontic cases: A retrospective study.

This article explores the integration of machine learning (ML) algorithms to aid in treatment planning and extraction decisions for anterior open bite cases, leveraging demographic, clinical, and radiographic data to predict treatment outcomes and informed decision-making. A retrospective study was conducted using patient data from the University of Illinois Chicago Department of Orthodontics. Data included demographic, clinical, and radiographic information from 115 anterior open bite patients who successfully completed their treatment. ML algorithms, including random forest, support vector machine, k-nearest neighbor, and convolutional neural networks (CNN), were trained on a subset of the data to predict treatment outcomes. Significant differences were observed in the percentages of males and females between the extraction and nonextraction groups and cephalometric variables between the two groups, which include maxillary depth, maxillary height, SN-palatal plane, facial angle, facial axis-Ricketts, FMA, total facial height, lower facial height, SNA, SNB, and SN-MP e ML algorithms examined consisted of CNN2, CNN1, and Random Forest, which demonstrated the highest accuracy rates (∼83%), while k-Nearest Neighbor had the lowest (∼73%). Key features influencing accuracy included crowding, SN-palatal plane, SNA, FMA, molar relation, and facial height measurements. The study's evaluation of AI algorithms showed that CNN2, CNN1, and random forest had an accuracy of approximately 83% in classifying extraction versus nonextraction cases. Notably, features such as U-crowding, L-crowding, SN-palatal plane, SNA, FMA, molar relation, total facial height, lower facial height, and facial axis-Ricketts were most influential in achieving accuracy rates comparable to traditional methods.

Interpretable Machine Learning Approach to Forecast Sustainable Power Generation

Burning fossil fuels like coal, oil, and natural gas releases significant CO2, driving carbon emissions in electricity production. This contributes to global warming, leading to climate change, extreme weather, rising sea levels, and harm to ecosystems and human health. Achieving zero emissions requires transitioning to low-carbon energy sources. This study uses various Machine Learning (ML) models for predicting low carbon electricity generation additionally eXpalinalbe Artificial Intelligence (XAI) to elucidate how ML model works and suggest some important factor contributing to the models outcome. Among several ML models RF outperforms other by achieving MSE of 2.782, RMSE of 2.782, MAE of 1.443 and R2 of 99.3%. Also GBM and XGB performed closely as RF. When applying XAI tools like SHAP and Shapash to RF it reveals some important factors such as Electricity from renewables (TWh), Eectricity from fussil fuels (TWh) Renewable energy share in the total final energy consumption (%), and Electricity from nuclear. Which are crucial for future energy planning and policy decisions. Future study can use a more extensive data set, investigate economic aspects, and include real-time data to enhance predictive model performance

Can Conflict Be Planned Away? A Critical Assessment of Participatory Land Use Planning in Swedish Forest Governance

A widespread governance response to land use conflict is to seek improved communication through the employment of dialogue-based instruments. In this paper, we interrogate the guiding presupposition that conflict can be planned away through a case study on the Reindeer Husbandry Plan (Renbruksplan), a tool used to address land use conflicts between industrial forestry and Indigenous Sámi reindeer herding. Drawing on critical policy analysis and environmental justice frameworks, we analyze the problematizations, silences, and effects emerging from the tool’s use in forestry planning and land use decisions. Our findings reveal that, operating in its current institutional and legal context, the tool offers limited improvements in procedural justice, exacerbates unequal distribution of burdens and benefits in terms of who gets to use forest resources, privileging a forestry-centered representation of the land use conflict. We therefore conclude that, in absence of institutional reform, the tool is likely to perpetuate conflicts and continue to reproduce the injustices embedded in Swedish forest and land use governance.

The evolution of green spaces within institutional plots in Yeka Sub-City, Addis Ababa, Ethiopia

Understanding the dynamics of urban green coverage is crucial for informing urban planning and design decisions that foster sustainable cities. This study employed Google Earth imagery and Exploratory Spatial Data Analysis (ESDA) to investigate the changes in urban green coverage within institutional plots in Yeka Sub-City, Addis Ababa, over a twenty-year period from 2003 to 2023. Subsequent image processing and analyses were conducted using GIS and ERDAS Imagine software. The findings revealed a gradual decline in green space coverage by 10.86 hectare, primarily attributed to the expansion of built-up areas and an increase in bare land. These changes suggest that the trend is driven by institutions constructing new facilities and optimizing land use within their compounds. The moderate positive spatial autocorrelation (Moran’s I value of 0.60) indicated that changes in green space were not randomly distributed but tended to cluster in specific areas. This study emphasizes the importance of preserving and enhancing existing green spaces within institutional compounds to promote environmental, social, and economic sustainability. By integrating green space preservation into urban planning and institutional land management practices, policymakers can ensure balanced urban development that aligns with environmental sustainability goals.

Mathematical Optimization of Monte Carlo Simulation Parameters for Predicting Stock Prices

Stock price prediction holds paramount significance for individual investors, guiding crucial decisions in financial planning and investment strategies. This research delves into the methodology of Monte Carlo simulation, a versatile tool in financial modeling, to assess its advantages and disadvantages in the context of predicting stock prices. The study employs Python code to demonstrate the step-by-step implementation of Monte Carlo simulations, emphasizing the mathematical optimization of parameters for enhanced accuracy. Results showcase a characteristic bell curve, offering a probabilistic perspective on potential outcomes. Comparative analyses with other forecasting models, such as graphic analysis, underscore the superior reliability of Monte Carlo simulation in evaluating risks and rewards. Furthermore, the paper explores the application of Monte Carlo simulation in real-world scenarios, such as portfolio optimization and retirement planning, highlighting its pragmatic value for individual investors navigating the complexities of the stock market. The research concludes by acknowledging the limitations of the approach and advocating for a comprehensive consideration of all relevant factors in financial decision-making. This exploration serves as a valuable resource for individual investors seeking informed insights into probabilistic forecasting methods for effective stock price predictions.

LLM Agents for Smart City Management: Enhancing Decision Support Through Multi-Agent AI Systems

This study investigates the implementation of LLM agents in smart city management, leveraging both the inherent language processing abilities of LLMs and the distributed problem solving capabilities of multi-agent systems for the improvement of urban decision making processes. A multi-agent system architecture combines LLMs with existing urban information systems to process complex queries and generate contextually relevant responses for urban planning and management. The research is focused on three main hypotheses testing: (1) LLM agents’ capability for effective routing and processing diverse urban queries, (2) the effectiveness of Retrieval-Augmented Generation (RAG) technology in improving response accuracy when working with local knowledge and regulations, and (3) the impact of integrating LLM agents with existing urban information systems. Our experimental results, based on a comprehensive validation dataset of 150 question–answer pairs, demonstrate significant improvements in decision support capabilities. The multi-agent system achieved pipeline selection accuracy of 94–99% across different models, while the integration of RAG technology improved response accuracy by 17% for strategic development queries and 55% for service accessibility questions. The combined use of document databases and service APIs resulted in the highest performance metrics (G-Eval scores of 0.68–0.74) compared to standalone LLM responses (0.30–0.38). Using St. Petersburg’s Digital Urban Platform as a testbed, we demonstrate the practical applicability of this approach to create integrated city management systems with support complex urban decision making processes. This research contributes to the growing field of AI-enhanced urban management by providing empirical evidence of LLM agents’ effectiveness in processing heterogeneous urban data and supporting strategic planning decisions. Our findings suggest that LLM-based multi-agent systems can significantly enhance the efficiency and accuracy of urban decision making while maintaining high relevance in responses.

Sociodemographic and socioeconomic heterogeneity in considering residents’ empowerment from ecotourism

ABSTRACT The purpose of this paper is to challenge the notion that residents experience empowerment from ecotourism to the same degree in central Ecuador. The recently amended Resident Empowerment through Tourism Scale was employed to assess potential differences in six empowerment dimensions (i.e. economic, psychological, sociological, cultural, political, and environmental) considering a community’s socio-demographic and socioeconomic variables (i.e. age, gender, level of education, urban/rural place of residence, length of residence, and ecotourism employment). Understanding heterogeneity in residents’ empowerment will better assist tourism managers and planners in decision making efforts. Onsite survey data were collected from residents living in and adjacent to Riobamba at their homes between May and August of 2022. The final sample included 486 completed questionnaires. Multiple analyses of variance tests were conducted to reveal any significant differences in empowerment across socio-demographic and socioeconomic variables. Ecuadorians living in urban areas adjacent to Chimborazo volcano who are younger and more educated reported feeling the highest level of empowerment through ecotourism. Interestingly, responses on length of residence showed that newer residents reported higher degrees of sociological, cultural, political, and environmental empowerment compared to long-term residents. In contrast, no significant differences were found in empowerment based on gender or employment within ecotourism.

Environmental imperative in the functioning of urban planning councils

The article considers the problem of inconsistency of legislation in the field of urban planning, insufficient consideration of environmental standards and the need to introduce the «environmental imperative» into the activities of urban planning councils. It is determined that existing regulatory legal acts are often of a recommendatory nature, which creates conditions for lobbying the interests of individual groups without due consideration of environmental requirements for urban planning safety. The role of architectural ecology as a new branch of science for the formation of environmentally sound decisions in urban planning is emphasized. The authors emphasize the need to improve legislation to ensure the sustainable development of settlements. The mandatory consideration of the environmental imperative as a key principle in the process of creating and approving urban planning documentation is proposed. It is noted that to ensure the environmental safety of territories, it is necessary to adapt urban planning activities to climate change, increase energy efficiency, create green zones, reduce greenhouse gas emissions and preserve biodiversity. The authors emphasize the importance of forming ecological thinking and culture among specialists in the field of architecture and urban planning for making environmentally sound decisions. The dynamic growth of the urbanization process requires constant analysis and timely response, both in the design of individual construction sites and in the process of creating urban planning documentation. That is why, the issue of finding modern methods and approaches to improving environmental legislation in the urban planning sector to ensure sustainable development is relevant. Thus, there is a need for in-depth study of the role of urban planning councils in ensuring sustainable development of urban areas and the implementation of environmental imperatives in their activities. This will contribute to increasing the efficiency of urban planning and improving environmental sustainability in cities. In conditions of environmental deterioration, it is also advisable to introduce landscape planning as one of the key elements of the environmental imperative. Landscape planning allows for the harmonious integration of natural and urbanized areas, contributes to the preservation of ecosystems, protection of biodiversity and improvement of the quality of the habitat. At the level of urban planning councils, this can be implemented through the development of integrated solutions that take into account the spatial characteristics of the territories.

What happened next? A survey of review clients evaluating impacts of rapid reviews.

End-user evaluation of the impact of evidence syntheses is critical to demonstrating value. This study presents results of a survey evaluating the impact of rapid reviews undertaken by two teams based in Melbourne, Australia and Hamilton, Canada. Clients were invited to participate in a short, written survey following delivery of a rapid review. Survey items encompassed reach, usefulness and format; interactions with the review teams; and overall satisfaction. Twenty-five completed surveys from 53 invitations were received pertaining to 19 rapid reviews conducted between September 2021 and October 2023. Topics encompassed COVID-19, health and behaviour change; reports were an average of 31 pages; and were delivered over an average of 62 days. Evaluation findings were positive, with high satisfaction with reports and service delivery; very high satisfaction with report structure and length; good evidence of reach (reports read by decision makers and cited in other documents); and evidence that the rapid reviews made contributions to strategic planning, policy and program funding decisions. Rapid reviews are making impactful contributions, alongside other inputs, to policy and practice. Further research is required to build this evaluation dataset; examine the balance between timeliness and methodological rigor in evidence synthesis; and explore models of delivery and capacity within and outside of government. It is also critical to promote implementation efforts to harness the full potential of rapid reviews and other evidence syntheses to impact the lives of citizens.

Preconception Care: A Concept Analysis of an Evolving Paradigm.

To clarify the concept of preconception care and develop a precise and inclusive definition to improve its implementation and impact on reproductive health outcomes. This concept analysis paper employs Rodgers' evolutionary method to analyse the concept of preconception care, examining its historical evolution, attributes, antecedents and consequences. A comprehensive literature review was conducted using databases such as Cumulative Index to Nursing and Allied Health Literature, Scopus, MEDLINE and Google Scholar, covering publications from 2012 to 2024. Data extraction involved identifying surrogate and related terms, attributes, antecedents and consequences of preconception care. A total of 1520 publications were retrieved, with 166 meeting eligibility criteria. Using systematic random sampling, 40 articles were selected for in-depth analysis. The analysis revealed that preconception care encompasses several attributes: period-related (biological, individual, public health and intergenerational), target population-related (individual, public and intergenerational) and pathway-related (universal, targeted and comprehensive). Antecedents include desires for a healthy baby and family planning decisions. Consequently, it improves pregnancy outcomes and health equity and enhances community and intergenerational health. The proposed operational definition highlights preconception care as a proactive strategy to optimise health before pregnancy through targeted and inclusive interventions. Preconception care is a dynamic and multifaceted process that extends from immediate preconception periods to long-term health considerations. Addressing diverse needs and effectively improving health outcomes requires a tailored approach considering individual, public and intergenerational perspectives. A transparent and inclusive definition of preconception care will enable healthcare professionals, particularly nurses, to deliver more effective, culturally sensitive and equitable care. It will support advocacy for policy changes, resource allocation and educational initiatives to enhance preconception health. Addressing preconception care's complexities and diverse needs will foster a more comprehensive understanding and implementation of preconception care, ultimately improving reproductive health outcomes and promoting health equity across generations. As this concept analysis was derived from published articles, patients or the public were not involved in the study's design, conduct or reporting.

Bird Richness and Abundance in Urban Areas: Simulation-Based Conservation Strategies for an Italian Town.

In this study, we (a) inventoried the breeding bird community of a town located in Northern Italy using quadrat sampling, (b) quantified bird richness and abundance, (c) measured sample completeness, (d) tested whether the bird community assembly was driven by environmental filtering (i.e., local properties of every single quadrat), e) explained bird richness and abundance in light of the land cover types present in each quadrat, (f) disentangled the marginal effects of every land cover type, and (g) simulated the effects on birds of different planning decisions. We recorded 36 breeding bird species, of which 17 were resident, 10 were mid-range migrants, and 9 were trans-Saharan migrators. The sampling completeness estimated ranged from 82.73% to 99.66% depending on the estimator procedure. Environmental filtering affected significantly (p < 0.10) the bird community assembly. Generalized Additive Models (GAMs) explained both bird richness (R2 = 91.7%) and abundance (R2 = 87.4%) satisfactorily in light of the land cover types. Simulations based on GAMs showed that local planners can largely influence bird richness in the study area, with both positive (urban greening) and negative (urban densification and sprawl) strategies.

Decoding Wildlife Habitat Shifts in a Changing Environment Through Machine Learning

Climate change affects wildlife habitats. Data analytics and machine learning have been used in this study, which focused on different ecosystems in the state of Virginia. In this paper, we propose a holistic approach that integrates climate data from the Open-Meteo API and wildlife observations from the iNaturalist API during the years 2020-2023 using real-time data collection, machine learning models, and interactive visualization techniques. Our approach uses ensembling machine learning methods such as XGBoost, Random Forest, and Gradient Boosting classifiers with 85% accuracy using only climate variables as predictors of wildlife presence. Among them, very strong correlations between temperature patterns and observations of wildlife were found (r = 0.72, p &lt; 0.001); temperature range and seasonal timing explained about 65% of the model fit. We developed an interactive web-based dashboard using Dash that visualizes temporal trends and spatial distributions. This study informs conservation planning and habitat management decisions in the face of climate change and demonstrates how the integration of multi-analytical approaches and real-time data collection provides detailed insights into complex ecological relationships.

Forecasting Range Shifts in Terrestrial Alpine Insects Under Global Warming.

Anthropogenic planetary heating is disrupting global alpine systems, but our ability to empirically measure and predict responses in alpine species distributions is impaired by a lack of comprehensive data and technical limitations. We conducted a comprehensive, semi-quantitative review of empirical studies on contemporary range shifts in alpine insects driven by climate heating, drawing attention to methodological issues and potential biotic and abiotic factors influencing variation in responses. We highlight case studies showing how range dynamics may affect standing genetic variation and adaptive potential, and discuss how data integration frameworks can improve forecasts. Although biotic and abiotic factors influence individual species responses, most alpine insects studied so far are shifting to higher elevations. Upslope shifts are often accompanied by range contractions that are expected to diminish species genetic variation and adaptive potential, increasing extinction risk. Endemic species on islands are predicted to be especially vulnerable. Inferences drawn from the responses of alpine insects, also have relevance to species in other montane habitats. Correlative niche modelling is a keystone tool to predict range responses to planetary heating, but its limited ability to consider biological processes underpinning species' responses complicates interpretation. Alpine insects exhibit some potential to respond to rising temperatures via genetic change or phenotypic plasticity. Thus, future efforts should incorporate biological processes by using flexible hybrid niche modelling approaches to enhance the biological realism of predictions. Boosting scientific capability to envisage the future of alpine environments and their associated biota is imperative given that the speed and intensity of heating on high-mountain ecosystems can surpass our ability to collect the empirical data required to guide effective conservation planning and management decisions.

FAMILY CAREGIVER PERSPECTIVES ON THE PALLIATIVE CARE NEEDS OF HOSPITALIZED PERSONS LIVING WITH DEMENTIA

Abstract The progressive nature of dementia results in significant symptom burden, functional decline, and frequent hospitalizations, underscoring the need for specialized palliative care for persons living with dementia (PLWDs) and their caregivers. Existing studies, primarily retrospective analyses of health records or provider interviews, lack information on the critical psychosocial and social support contexts that significantly influence the palliative care needs of PLWDs. Addressing this gap, our study explored the perspectives of family caregivers on the palliative care needs of hospitalized PLWDs, informed by the Adapted Conceptual Model Integrating Palliative Care in Serious Illness and Multiple Chronic Conditions. We interviewed 37 caregivers of hospitalized PLWDs who were referred for palliative care. Caregivers identified primarily as female (66%) and caring for parents (58%), and as White (46%), Black (17%), Asian (5%), and Latino (31%). Most common challenges caregivers reported were about addressing health issues (58%), managing emotional difficulties (53%), and making care plan decisions (42%). Three related themes emerged from in-depth qualitative interviews: the complex care needs of PLWDs, including pain and symptom management; caregivers’ needs for support in discharge preparation; and challenges in making care transition decisions into facilities. These insights underscore the caregivers’ critical role in managing care and making decisions during critical care transitions for hospitalized PLWDs. The findings provide implications for practice and research including enhanced care coordination between primary, palliative, and discharge planning services in acute settings and future research on the impact of psychosocial and social support factors on palliative care outcomes for this vulnerable population.

Involvement Of Males In Utilization Of Maternal And Child Health Services: A Cross-Sectional Study Of Women’s Perspective

Background: Men in patriarchal societies of developing countries like India are often identified as decision makers in all aspects of life. Men’s supportive stance in MCH services utilization plays crucial role in reduction of maternal and child mortality. Aim: To study the involvement of males in maternal and child healthcare services. Objective: To study sociodemographic profile of study participants and knowledge and practices of their husbands about MCH services. Material and methods: The cross-sectional study was conducted among 130 married women having one living child less than 2 year of attending immunization clinics of Government medical college and hospital, Miraj. Information about socio-demographic and reproductive health characteristics, male participation in reproductive and child health care services was collected entered in MS excel and analyzed. Results: Among the females 70% were below 25 years of age and age of the husbands of 71% participants were less than 30 years. 64% females were belonging to Muslim religion, 84% were residing in urban area, 82% stayed in joint family, 58% educated up to secondary school, 67% belongs to socio-economic class IV. 61% husbands were decision maker in family planning, 78% husbands accompanied for ANC visit,79% husbands knew about the govt. MCH scheme 94% husbands knew about increased need for food &amp; rest by pregnant women, 63% were having knowledge about complications of pregnancy and delivery. But only 22% husbands accompanied for child’s immunization. Only 34% husband help in domestic work, 63% paid attention on diet/food frequency of their wives and 51%encouraged &amp; paid attention on adequate rest of their wives. Conclusion: Men are aware of various aspects of antenatal and intra natal care, their husband’s involvement during antenatal period was found to be good but it decreases during immunization phase of the child. Involvement for helping in domestic work, encouraging for adequate rest &amp; food during ANC was also less. So, creating specific Health education and SBCC activities about maternal &amp; child health among men may enhance their positive involvement.

Опыт зарубежных исследований в области акустических архитектурных решений в школах для детей с аутизмом

Nowadays, teaching children with autism in secondary schools is a new practice for Russia. Its implementation requires special knowledge and skills from all involved specialists. It also requires special architectural and design practices at all construction stages: from the project to the object itself. After competent planning decisions, the most important element is acoustic comfort in speech therapy and other rooms for inclusive purposes. Students with special educational needs, including autism, have a high level of sensitivity to noise with strong tonal, impulsive, or intermittent characteristics. A correct controlled acoustic environment is necessary to preserve the normotypic behavior of students with autism in the schools. Indeed, we should consider their needs when performing functional zoning and avoid artificial formation of a quiet environment for the learning process. Hence, generalising communication skills in an acoustically unchanged environment will ensure maximum students’ integration into social interactions both inside and outside the school.

Wastewater treatment plant site selection using advanced decision tree machine learning and remote sensing techniques.

Wastewater treatment plants in Coimbatore South are under pressure from rapid urbanization, inadequate infrastructure, and industrial pollution, leading to environmental and public health concerns. This study aimed to identify suitable locations for wastewater treatment plants using a combination of machine learning, remote sensing, and GIS-based multicriteria decision analysis (MCDA). Several datasets were analysed, with the analytical hierarchy process (AHP) assigning weights to factors such as slope (18.51) and elevation (31.39), which were found to be crucial in site selection. The study classified the suitability of sites into five categories, with the western region being the most favourable due to its low elevation (147 to 200m), gentle slopes (0-3%), and substantial land availability (approximately 309.00 sq. km). Overall, the site suitability analysis revealed that 14.48% (110.2 sq. km) of the area falls within "Very High Favourable Zones," while 11.21% (85.3 sq. km) is categorized as "High Favourable Zones." Moderate and low favourable zones account for 9.63% (73.3 sq. km) and 24.04% (182.9 sq. km), respectively. The remaining 40.57% (308.7 sq. km) is considered "Very Low Favourable Zones." These results can guide urban planning decisions, highlighting the importance of factors such as land availability, population growth, wastewater volume, and flood vulnerability. Integrating GIS technology with decision-making processes enhances the strategic placement of urban utilities, ensuring long-term sustainability for Coimbatore South's wastewater management.