Regional Classification Research Articles

Women entrepreneurship in the internationalization of SMEs: a bibliometric analysis for future research directions

PurposeThis study aims to systematically review papers on women’s entrepreneurship in the internationalization of small and medium enterprises (SMEs) by examining the research profile (i.e. publication trends, publishers, domain and quality of journals, methodologies and units of analysis and regional classifications), internationalization entry modes and the theories underpinning, key variables using AMO framework (i.e. antecedents, mediators, moderators and outcomes) and suggestions for potential future studies.Design/methodology/approachThis study used bibliometric analysis by selecting 62 relevant papers from 3,016 papers collected from the Web of Science, Scopus, Google Scholar and EBSCO databases. Content analysis was conducted to identify key research issues and gaps, which were then mapped on an AMO framework to address potential future research.FindingsThis study found that the number of papers published during the period 1994–September 2022 fluctuated, indicating an increasing trend of women’s entrepreneurship research in the internationalization of SMEs being published in various reputable journals by well-known publishers. This study also found several alternatives of internationalization entry modes, although export is still the most widely used alternative. Various theories that underlie this research include internationalization theory, resource-based theory, feminist theory and international entrepreneurship. This study proposes a future research framework on women’s entrepreneurship in the internationalization of SMEs: AMO.Originality/valueThis study contributes to the intersection of gender-focused international business and entrepreneurship domains. This study proposes a conceptual model of women’s entrepreneurship in internationalization of SMEs by mapping the antecedents, mediators/moderators and outcomes and offers promising opportunities for future research directions.

Image semantic segmentation using deep learning technique

With the deepening research on image understanding in many application fields, including auto drive system, unmanned aerial vehicle (UAV) landing point judgment, virtual reality wearable devices, etc., computer vision and machine learning researchers are paying more and more attention to image semantic segmentation (ISS). In this paper, according to the different region generation algorithms, the regional classification image semantic segmentation methods are classified into the candidate region method and the segmentation mask method, according to different learning methods, the image semantic segmentation methods based on super pixels are divided into fully supervised learning method and weakly supervised learning method. The typical algorithms in these various categories are summarized and compared. In addition, this paper also systematically expounds the role of DL technology in the field of ISS, and discusses the main challenges and future development prospects in this field.

원도심학교 활성화에 관한 교육관계자 및 전문가 인식연구

The purpose of this study is to analyze how school education subjects and related groups recognize the original city school and what its meaning is in relation to the original city school revitalization plan. In addition, it is to explore what factors can affect the revitalization of the original city center school. The opinions of school officials and experts were collected through a group interview format, and the results were examined based on the qualitative data analysis method.
 As a result of the study, the first problem facing the original downtown school in Happy City is that the phenomenon of ‘the poor and the poor’ is occurring by misclassifying the support area. The wrong regional classification also caused problems in granting additional points for promotion to teachers, making it difficult to secure excellent teachers in the original city center school. Second, another problem in the original downtown area of Happy City is that the educational environment is being damaged due to the revitalization of the tourism industry. In order to revitalize the original city school, the educational subjects in the school must first be able to voluntarily promote the project. In order to promote the school revitalization project, at least three teachers, including the principal, must be provided with conditions for continuous power. In addition, in the case of Happiness City, rural areas problem should be solved preferentially because these areas where promotion additional points are given are incorrectly selected. It is necessary to secure the conditions for devotional teachers to continue working in the original city school. Finally, in order for the original city school to be activated, it is necessary to be able to organize and implement effective programs suitable for the original city school. Considering that the proportion of students with poor family circumstances is high, education and welfare programs should be strengthened in original city schools than in other schools.

Extending regional habitat classification systems to ocean basin scale using predicted species distributions as proxies

The patchy nature and overall scarcity of available scientific data poses a challenge to holistic ecosystem-based management that considers the whole range of ecological, social, and economic aspects that affect ecosystem health and productivity in the deep sea. In particular, the evaluation of, for instance, the impact of human activities/climate change, the adequacy and representativity of MPA networks, and the valuation of ecosystem goods and services is hampered by the lack of detailed seafloor habitat maps and a univocal classification system. To maximize the use of current evidence-based management decision tools, this paper investigates the potential application of a supervised machine learning methodology to expand a well-established habitat classification system throughout an entire ocean basin. A multi-class Random Forest habitat classification model was built using the predicted distributions of 6 deep-sea fish and 6 cold-water corals as predictor variables (proxies). This model, found to correctly classify the area covered by an existing European seabed habitat classification system with ~90% accuracy, was used to provide a univocal deep-sea habitat classification for the North Atlantic. Until such time as global seabed mapping projects are complete, supervised machine learning approaches, as described here, can provide the full coverage classified maps and preliminary habitat inventories needed to underpin marine management decision making.

Disentangling Effects of Natural Factors and Human Disturbances on Aquatic Systems—Needs and Approaches

Disentangling the effects of natural factors and human disturbances on freshwater systems is essential for understanding the distributions and composition of biological communities and their relationship with physicochemical and biological factors. As the spatial extent of ecological investigations increases from local to global scales, efforts to account for the increasing influence of natural factors become more important. This article synthesizes the current knowledge and commonly used approaches for disentangling these effects on aquatic systems. New understanding has been facilitated by the availability of large-scale geospatial landscape databases that facilitate regional analyses and classifications in conjunction with novel approaches to identify reference conditions and statistical partitioning analyses. This synthesis begins with a summary of how natural factors and human disturbances interactively affect aquatic systems. It then provides an overview of why it is essential to separate the effects of natural factors and human disturbances and a description of examples of landscape databases that make the separation of natural and human factors feasible. It last synthesizes currently-used common approaches for separating the effects of natural factors from human disturbances. Our synthesis assembles representative approaches to disentangling human disturbances in one place to provide new insights that stimulate integrated uses of multiple approaches and the development of new approaches so that management actions can be taken to protect and restore aquatic ecosystem health.

Multi‐Source Mapping of Peatland Types Using Sentinel‐1, Sentinel‐2, and Terrain Derivatives—A Comparison Between Five High‐Latitude Landscapes

AbstractMapping wetland types in northern‐latitude regions with Earth Observation (EO) data is important for several practical and scientific applications, but at the same time challenging due to the variability and dynamic nature in wetland features introduced by differences in geophysical conditions. The objective of this study was to better understand the ability of Sentinel‐1 radar data, Sentinel‐2 optical data and terrain derivatives derived from Copernicus digital elevation model to distinguish three main peatland types, two upland classes, and surface water, in five contrasting landscapes located in the northern parts of Alaska, Canada and Scandinavia. The study also investigated the potential benefits for classification accuracy of using regional classification models constructed from region‐specific training data compared to a global classification model based on pooled reference data from all five sites. Overall, the results show high promise for classifying peatland types and the three other land cover classes using the fusion approach that combined all three EO data sources (Sentinel‐1, Sentinel‐2 and terrain derivatives). Overall accuracy for the individual sites ranged between 79.7% and 90.3%. Class specific accuracies for the peatland types were also high overall but differed between the five sites as well as between the three classes bog, fen and swamp. A key finding is that regional classification models consistently outperformed the global classification model by producing significantly higher classification accuracies for all five sites. This suggests for progress in identifying effective approaches for continental scale peatland mapping to improve scaling of for example, hydrological‐ and greenhouse gas‐related processes in Earth system models.

Regional classification and competitiveness of port cluster: a case study of China's coastal ports

ABSTRACT Many studies on port competitiveness try to understand which aspects will affect the competitive performance of ports. However, there are few studies from the perspective of port clusters. This paper, by reviewing the history of port competitiveness research to explain the need for research in the context of port clusters, and construct an indicator system from the perspective of port cluster development in terms of port throughput capacity, scale, development potential and intra-cluster development environment. The entropy TOPSIS method is used to objectively evaluate the competitiveness level of each port within the major port clusters in China and try to improve the Boston matrix model to identify each port type. We found that from 2014 to 2019, cash cow ports such as Shanghai Port, Shenzhen Port, and Ningbo Zhoushan Port maintained great development status. The port competitiveness of the Bohai Bay Port cluster is not prominent, while the port competitiveness of the Yangtze River Delta port cluster and Pearl River Delta port cluster shows obvious polarisation. The prediction results based on the improved Boston matrix model are consistent with the actual situation of port development, which can assist future decision-making. The results reveal the relationship between cooperation and competition in the process of port integration.

Assessment of integrated patterns of human-animal-environment health: a holistic and stratified analysis

BackgroundData-driven research is a very important component of One Health. As the core part of the global One Health index (GOHI), the global One Health Intrinsic Drivers index (IDI) is a framework for evaluating the baseline conditions of human-animal-environment health. This study aims to assess the global performance in terms of GOH-IDI, compare it across different World Bank regions, and analyze the relationships between GOH-IDI and national economic levels.MethodsThe raw data among 146 countries were collected from authoritative databases and official reports in November 2021. Descriptive statistical analysis, data visualization and manipulation, Shapiro normality test and ridge maps were used to evaluate and identify the spatial and classificatory distribution of GOH-IDI. This paper uses the World Bank regional classification and the World Bank income groups to analyse the relationship between GOH-IDI and regional economic levels, and completes the case studies of representative countries.ResultsThe performance of One Health Intrinsic Driver in 146 countries was evaluated. The mean (standard deviation, SD) score of GOH-IDI is 54.05 (4.95). The values (mean SD) of different regions are North America (60.44, 2.36), Europe and Central Asia (57.73, 3.29), Middle East and North Africa (57.02, 2.56), East Asia and Pacific (53.87, 5.22), Latin America and the Caribbean (53.75, 2.20), South Asia (52.45, 2.61) and sub-Saharan Africa (48.27, 2.48). Gross national income per capita was moderately correlated with GOH-IDI (R2 = 0.651, Deviance explained = 66.6%, P < 0.005). Low income countries have the best performance in some secondary indicators, including Non-communicable Diseases and Mental Health and Health risks. Five indicators are not statistically different at each economic level, including Animal Epidemic Disease, Animal Biodiversity, Air Quality and Climate Change, Land Resources and Environmental Biodiversity.ConclusionsThe GOH-IDI is a crucial tool to evaluate the situation of One Health. There are inter-regional differences in GOH-IDI significantly at the worldwide level. The best performing region for GOH-IDI was North America and the worst was sub-Saharan Africa. There is a positive correlation between the GOH-IDI and country economic status, with high-income countries performing well in most indicators. GOH-IDI facilitates researchers' understanding of the multidimensional situation in each country and invests more attention in scientific questions that need to be addressed urgently.Graphical

Regional Classification of Serbian Railway Transport System Through Efficient Synthetic Indicator

The railway transport system is one of the most important elements in the development of the economy and the social space of any area. The main objective of the study is to analyse the regional differentiation in railway development in Serbia with causal interference. The research has been conducted based on secondary data collected from multiple sources, and the existing synthetic Indicator was applied to classify eight states based on their railway infrastructural status. An alternative synthetic indicator approach has been proposed and found to be more efficient than the existing synthetic Indicator. The causality of such unequal development has been analysed through a correlation test by defining the composite infrastructure index. The analysis revealed that railway infrastructure significantly influences Serbia's economic and social development. The service area of railway infrastructure indicates the potential zone for future growth.

Difference between global and regional aerosol model classifications and associated implications for spaceborne aerosol optical depth retrieval

AbstractAerosol model are generally adopted to describe the physical and optical characteristics of aerosols in different regions, and this is important for climate and environmental studies as well as satellite aerosol retrieval. However, current mainstream aerosol retrieval algorithms still lack differentiated and dynamic descriptions of aerosol models, and the aerosol optical depth (AOD) retrieval accuracy is hence largely compromised. Therefore, in this study, we aimed to investigate the difference between global and regional aerosol model classifications and the impact of these differences on satellite AOD retrieval. We developed global and regional (for China) aerosol models through the K-means cluster analysis method, and used the 6SV radiative transfer model based on these global and regional aerosol models to simulate air pollution events. The cluster analysis revealed that both global and regional aerosols can be clustered into five main aerosol types with different optical and physical parameters. The aerosol types include strongly absorbing, moderately absorbing, weakly absorbing, dust and coarse-fine mixed aerosols. These two aerosol models exhibited distinct seasonal variations. Comparing the clustering results obtained with the two models at five Aerosol Robotic Network (AERONET) sites, we found that the proportion of strongly absorbing aerosols in the global results is low. According to the China clustering results, a higher proportion of strongly absorbing aerosols occurred in autumn, winter, and early spring, and dust aerosols attained an increasing proportion in spring. Comparing the aerosol parameters with observational data, the regional aerosol model performed better than the global aerosol model. Simulations of AOD during a pollution episode and in different seasons at the Beijing-CAMS site with the 6SV radiative transfer model further confirmed that the regional aerosol model achieved a better simulation accuracy than the global model. The findings suggest that regional aerosol models can provide a useful reference for satellite remote sensing algorithm, climate change and air pollution assessment, and that the dynamic description of the aerosol model is a key parameter in satellite AOD retrieval algorithms.

No stable coastline in the Monterrey Trough during Late Jurassic to Early Cretaceous! - Comment on Ocampo-Díaz et al. (2022): doi: 10.1016/j.jsames.2022.103759

The following is a comment on the paper: “A wave to tidal influenced deltaic coastline in a carbonate environment: The Late Jurassic to Early Cretaceous La Casita Formation, northeastern Mexico” by Ocampo-Díaz et al. (2022). The authors presented a sedimentological and paleogeographical study of the La Casita Formation, Monterrey Salient, Sierra Madre Oriental. Although this study offers a detailed facies analysis, it shows some need for discussion regarding the study area's stratigraphic and regional geological classification. The article mentions missing or insufficiently treated geological units such as the Carbonera Formation, San Juan Lentil, and Galeana Member. The Coahuila Block, Sabinas Basin, and Tamaulipas Arch are in an erroneous stratigraphic and regional geological context. This comment takes up these weak points and tries to correct them. This comment offers the missing stratigraphic table of the La Casita/Carbonera and the La Casita/Taraises boundary. The siliciclastic deposition of the La Casita Formation ends in the Early Berriasian. Finally, the faulty paleogeographical reconstructions of the authors are countered by an alternative model which shows the pro- and retrograding sedimentation of the La Casita Formation during Tithonian to Berriasian, the development of the San Juan Lentil at Late Berriassian, and the development of the open shelf carbonates of the Taraises Formation, such as the contemporary Carbonera Formation and Galeana Sandstone Member (Valanginian to Hauterivian).

The Ordovician of Scandinavia: a revised regional stage classification

Abstract The Ordovician of Scandinavia (i.e. Denmark, Norway and Sweden) has been investigated for over two centuries and, through time, various chronostratigraphic schemes have been introduced, facilitating regional correlation. However, a modern chronostratigraphy has never been proposed. Here, we delineate ten regional stages for the Ordovician of Scandinavia, comprising, in ascending order, the Slemmestadian, Ottenbyan, Billingenian, Volkhovian, Kundan, Segerstadian, Dalbyan, Moldåan, Jerrestadian and Tommarpian. We propose to discontinue the use of the term Hunnebergian Regional Stage despite its Scandinavian origin; this interval is included in the new Ottenbyan Stage. The base of each stage, as (re)defined here, is selected to coincide with the appearance of a characteristic fossil taxon and delimited at the top by the base of the overlying stage. The stage boundaries generally coincide with or approximate to significant changes in the depositional environment that are recognizable across Scandinavia from the carbonate platform to the foreland basin. Local efficacy has been the primary criterion for the recognition of Scandinavian stage boundaries rather than approximating to the global or East Baltic stage boundaries. It is proposed to abolish the Baltoscandian regional series and subseries, as correlation with the global series is sufficiently precise to make these higher rank regional schemes redundant.

Geomorphological Classification of Western Western Doon of Dehra, Uttarakhand (India)

Morphological units are those sections of landforms, which have uniform morphological property. The identification of morphological or terrain units is an exercise of regional classification of terrain with similar properties on varying scales. Morpho units enable the planners to determine land use planning, infrastructure development and natural resource management. Such type of terrain classification also highlights the hazardous unit, particularly in mountainous areas. Doon of Dehra is an important geomorphic unit in terms of both natural and cultural point of view. Dehradun, which is the capital city of Uttarakhand is also situated in this geomorphic unit. So, it becomes important to delineate the unit in smaller geomorphic units for proper land use planning and infrastructural development and to mitigate the hazards as well. Topographical sheets published by the government of India are the database of study. In the present study ‘principle component analysis’ based on the centroid method of Holziger and Herman (1941) has been adopted as this is manageable manually. The components, like - relief, drainage derivatives and slope has been taken for meso and micro level division of area into morpho units. The study area consists of 4 major morpho units and 28 micro units.

Методология геоинформационного мелкомасштабного картографирования геосистем для управления экологическими рисками

Within the framework of the thematic section “Ecological state of the natural environment, natural environmental risks” of the electronic geographical Atlas of North and Northeast Asia, a methodology for small-scale mapping of geosystems has been developed as a procedure for preparing scientific information about a natural geographical object to ensure environmental risk management. The scientific basis of the research is the theory of spatial and temporal organization and the methodology of constructive integration mapping of geosystems. Natural ecological risk is understood as the probability of negative changes in the structure and functioning of geosystems in the case of natural or anthropogenic phenomena and processes in the habitat. The methods of creation, structure, and content of the unified information and cartographic system of the Mongolian-Siberian region of geosystem content of scale 1:5 000 000 have been developed. For these purposes, geographically and conceptually inconsistent maps of the geosystems of the regions of Siberia and Mongolia were analyzed. The patterns of spatial and temporal organization, regional features of functional differentiation of natural systems are revealed. Based on the results of the study, scientific generalizations were made concerning the description of geosystems of regional rank, their typification, classification, and zoning. The prognostic potential of small-scale maps of geosystems in relation to geographical studies of natural environmental risks based on the procedure for analyzing geosystems by typological functional, structural, and dynamic features is revealed. The characteristics of natural factors of the intensity of functioning, ecological functions of geosystems served as indicators in the environmental regulation of the use of natural systems, allowed to correctly substantiate environmental recommendations to minimize undesirable consequences. A specialized classification is demonstrated—a legend and a zoning map of the territory of the Mongolian-Siberian region, reflecting the sequence of multi-stage cartographic studies of natural environmental risk and the structuring of physical and geographical information. The created maps make up the basic set in the structure of spatial data of the electronic Atlas, new maps of geosystems can be created on their basis.

A predictive decision support system for coronavirus disease 2019 response management and medical logistic planning.

Coronavirus disease 2019 demonstrated the inconsistencies in adequately responding to biological threats on a global scale due to a lack of powerful tools for assessing various factors in the formation of the epidemic situation and its forecasting. Decision support systems have a role in overcoming the challenges in health monitoring systems in light of current or future epidemic outbreaks. This paper focuses on some applied examples of logistic planning, a key service of the Earth Cognitive System for Coronavirus Disease 2019 project, here presented, evidencing the added value of artificial intelligence algorithms towards predictive hypotheses in tackling health emergencies. Earth Cognitive System for Coronavirus Disease 2019 is a decision support system designed to support healthcare institutions in monitoring, management and forecasting activities through artificial intelligence, social media analytics, geospatial analysis and satellite imaging. The monitoring, management and prediction of medical equipment logistic needs rely on machine learning to predict the regional risk classification colour codes, the emergency rooms attendances, and the forecast of regional medical supplies, synergically enhancing geospatial and temporal dimensions. The overall performance of the regional risk colour code classifier yielded a high value of the macro-average F1-score (0.82) and an accuracy of 85%. The prediction of the emergency rooms attendances for the Lazio region yielded a very low root mean square error (<11 patients) and a high positive correlation with the actual values for the major hospitals of the Lazio region which admit about 90% of the region's patients. The prediction of the medicinal purchases for the regions of Lazio and Piemonte has yielded a low root mean squared percentage error of 16%. Accurate forecasting of the evolution of new cases and drug utilisation enables the resulting excess demand throughout the supply chain to be managed more effectively. Forecasting during a pandemic becomes essential for effective government decision-making, managing supply chain resources, and for informing tough policy decisions.

Cluster Analysis of Drought Damage to Local Governments Across the Country Using the National Drought Information Statistics

Recently, Korea has regularly published statistics on the current state of annual droughts that have been occurring since 2018 in the National Drought Information Statistics. For a clear categorization of drought damage, it is necessary to first establish the major factors that affect the degree of damage. In this study, damage-related data from the National Drought Information Statistics Collection (2018-2020) were examined to obtain the regional characteristics of water use resulting in regional drought damage to 161 local governments in Korea. Cluster analysis was performed, and a hierarchical clustering algorithm was used for the regional classification of drought damage. The factor composition was analyzed by dividing it into four major methods to represent the drought damage caused by general, living, and agricultural droughts. The analysis confirmed that regional clustering appeared according to the characteristics of water use and past damage history. If such clustering-based regional damage classification information and factors affecting drought damage by water are considered, it is expected that the damage sensitivity of local governments according to the drought situation and regional water use characteristics can be presented quantitatively.

미기후를 고려한 권역별 철근콘크리트조 리모델링 성능 기준안 연구 -부천시를 중심으로-

The purpose of this study is to propose remodeling performance considering the microclimate of each region in Bucheon. In order to propose the performance criteria, regional classification of previous microclimate studies was used. The insulation level was analyzed during the remodeling of the reinforced concrete structure using the lowest temperature by region. The appropriate insulation level was suggested as a level at which mold does not occur through 2d heat transfer analysis. In the analysis, different surface transfer resistance values were used to consider the living environment of occupants. As a result of the analysis, different insulation thicknesses were required for each District in Bucheon. This means that regional microclimate considerations are required for economic remodeling. This study proposed an appropriate insulation thickness that escapes the surface temperature defect, but has a limitation in that it did not consider the defect inside the wall.

Petrological characterization of Fe–Ti oxides in metamafic rocks from the Nw borborema province, Ne Brazil

The coexistence of Fe–Ti oxide phases constitutes an important petrogenetic tool to understanding the evolution of metamafic rocks. In the NW part of the Borborema Province, NE Brazil, metamafic rocks occur in two distinct zones: the Cariré Granulite and the Forquilha Eclogite. The present work characterizes the sequence of crystallization involving Fe–Ti oxides of these lithotypes and constrains its redox conditions using the ilmenite-rutile oxybarometer. Multiphase association of Fe–Ti oxides of a mafic granulite from the Cariré Zone and a retrograded eclogite from the Forquilha Zone comprise ilmenite, magnetite, rutile, spinel, and hematite. Rutile occurs as inclusions, surrounded by vermicular, xenomorphic or coronitic titanite, within ilmenite crystals. The redox conditions set by the sequence of Fe–Ti oxides crystallization and the rutile-ilmenite oxybarometer are compared to the FMQ (fayalite-magnetite-quartz) buffer, indicating an increase in oxygen fugacity from peak to retrograde conditions. Our characterization stablishes (re)crystallization and oxyexsolution sequences for the Fe–Ti oxides and the rutile-ilmenite oxybarometer in the mafic granulite from the Cariré Zone suggests oxygen fugacity of FMQ – 3.6. The retrograded eclogite indicates a more reduced environment for the Forquilha Zone, in which the low Fe3+ content of ilmenite hampers the estimation of redox conditons. The constrained ΔFMQ is within the range of common redox compositions for subduction-related metamafic rocks, accordingly with the regional geochemical classification reported in the literature to the Cariré and Forquilha zones.

Levels of automation and design standards in transport

The existing scheme of classification of transport routes (railways and roads) depending on the intensity of the traffic load — flows of vehicles and speeds of their movement — is shown. It is noted that there are no universal classifications of transport routes at the global level within the same transport mode. There are national, regional and network-based pathway classification systems. Due to the development of digital traffic management systems in transport, classifications of lines of transport depending on the level of automation of the rolling stock have started to appear. The classifications adopted in the Transport Strategy of the Russian Federation for the main modes of transport — land (railways, roads), water (maritime and river transport) — considered. Bearing in mind the railway transport of the Russian Federation, the set of basic concepts adopted in the Provisional National Standard of the Russian Federation given the levels of automation in the control system of railway rolling stock in automatic and remote modes, predetermining the requirements for railway infrastructure — structures and devices, respectively. A similar situation found in the classification system for smart roads proposed by the International Road Congress Association. A conclusion on the need to revise the normative documentation for the design of construction (reconstruction) of communication lines to include a calculated level of automation formulated.

How to Create a Regional Diatom-Based Index: Demonstration from the Yuqiao Reservoir Watershed, China

Diatom-based indices derived from the percentage of diatom taxa groups can be used to assess water quality. As some diatoms are location-dependent, such diatom indices are correspondingly location-dependent and the regional classification of taxa group is thereby needed. This study aims to demonstrate how to create a regional diatom assemblage index (YRDAI) based on a case study from the Yuqiao Reservoir watershed, China. Herein, we proposed a simple taxa classification approach based on the correlation between pollutant concentration and diatom abundance, and compared it with the traditional approach based on the coexistence index. Using the two approaches, a total of 34 diatom taxa groups were reclassified for localization of the well-known DAIpo index. The YRDAI was then derived from the reclassified diatom taxa groups. The results in the Yuqiao Reservoir watershed showed that the correlation-based YRDAIcor scores could better reflect the pollution levels of COD and TP than the coexistence-based YRDAIco scores and the original DAIpo scores with the unreclassified taxa groups. It can be expected that the precision of YRDAI can be improved with the accumulation of the diatom data, and the above approaches can be applied to other watersheds for making their own regional indices.