Artificial intelligence for identifying hydrologically homogeneous regions: A state‐of‐the‐art regional flood frequency analysis

Cassalho, Felício; Beskow, Samuel; Mello, Carlos Rogério de; Moura, Maíra Martim de; Oliveira, Leroi Floriano; Aguiar, Marilton Sanchotene de

doi:10.1002/hyp.13388

Cited by 28 publications

(12 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Through the analysis of the data of college students' ideological and mental health support services in the results in Figure 7 , it can be found that, under the star continuous analysis model of fuzzy cluster analysis algorithm, the data error degree of the experimental group and the control group will show strong disturbing characteristics with the internal correlation, and its internal data coupling error strategy will also have low-level discrete driving representation. Therefore, the accuracy of the result correlation is very high, while the deviation of the result correlation of the control group is very obvious [ 26 ]. Then we can know that, in the process of practical application, we need to combine the deviation of active participation of different types of college students' ideological and mental health support services to realize its quantitative evaluation and reliability disposal.…”

Section: Results Analysis and Discussionmentioning

confidence: 99%

College Students’ Mental Health Support Based on Fuzzy Clustering Algorithm

2022

Contrast Media & Molecular Imaging

View full text Add to dashboard Cite

There are some problems in the active participation of college students in ideological and mental health support services in China, such as low attention, low participation, and high data redundancy. Based on this, this paper studies the active participation of college students’ ideological and mental health support service based on fuzzy cluster analysis algorithm. Compared with the disadvantages of the current mainstream discrete optimization analysis models on mental health (such as high-dimensional enterprise model, Dajiaweikang model, and short-range group control model), which need to set the known data gradient interval, this paper creatively adopts the fuzzy cluster analysis algorithm, based on the characteristics of different types of college students’ ideological and mental health problems. Combined with the improved star discrete analysis model, this paper constructs the active participatory evaluation strategy of college students’ ideological and mental health support services. On this basis, the model can not only record and store the participatory data of ideological and mental health support for students of different grades but also match and track different types of data based on special framework conditions, so as to achieve numerical normal analysis and directional matching for the data coupling mode of college students’ ideological and mental health support services. On the other hand, the Planck constant factor is used to classify different types of ideological and psychological factor data, and combined with the idea of fuzzy clustering, the hierarchical analysis and quantitative calibration of different types of data groups are realized, so as to improve the reliability and authenticity of the active participation in college students’ mental health support services. The results show that this star discrete analysis model can analyze the active participation of college students’ ideological and mental health support services according to the data matching degree of different levels and can effectively improve the analysis efficiency of data vectors. Compared with the traditional research methods on the active participation of college students’ ideological and mental health support services, this method can realize the matching and tracking of different types of data, so as to make a numerical and normal analysis on the data coupling mode of college students’ ideological and mental health support services.

show abstract

Section: Results Analysis and Discussionmentioning

confidence: 99%

College Students’ Mental Health Support Based on Fuzzy Clustering Algorithm

2022

Contrast Media & Molecular Imaging

View full text Add to dashboard Cite

show abstract

“…Furthermore, each of the regionalization approaches (ROI, CCA, CA) considered in this study has several variants. One could attempt preparing ensembles corresponding to different variants of ROI (e.g., Cunderlik & Burn, 2006; Durocher et al., 2018; Formetta et al., 2018; Mostofi & Burn, 2019; Zrinji & Burn, 1996), CCA (e.g., Han et al., 2020; Ouali et al., 2016; Ouarda et al., 2000; Ribeiro‐Corréa et al., 1995; Shu & Ouarda, 2007), and CA (e.g., Basu & Srinivas, 2014, 2016; Cassalho et al., 2019; Farsadnia et al., 2014; Rao & Srinivas, 2006a; Wazneh et al., 2015) for investigating improvement in regions derived by application of FEC to those individual ensembles and their possible combinations.…”

Section: Discussionmentioning

confidence: 99%

Fuzzy Ensemble Clustering Approach to Address Regionalization Uncertainties in Flood Frequency Analysis

Kiran

Srinivas

2021

Water Resources Research

View full text Add to dashboard Cite

Estimates of flood quantiles are necessary at various locations/hotspots in river basins for applications related to hydrological design and risk assessment of water resources systems. Desired flood quantiles could be estimated by frequency analysis of the available at-site data for locations having sufficient historical information on flows. However, for an ungauged or sparsely gauged location, the flood quantiles' prediction remains one of the challenging problems in hydrology. A variety of regional flood frequency analysis (RFFA) approaches are available for addressing this challenge. The approaches generally involve (i) regionalization, which refers to identification of a group of watersheds (i.e., region) resembling the target site's watershed in terms of attributes/characteristics influencing flood, and (ii) regional frequency analysis (RFA), which involves pooling of flood-related information from sites in the region and using it to estimate desired flood quantile(s) at the target site. Approaches to watershed regionalization for RFFA include those based on regression analysis (method of residuals), canonical correlation analysis (CCA), region of influence (ROI) and its extensions, hierarchical approach, and cluster analysis. Several articles and books provide an overview of the literature on regionalization of watersheds and issues related to RFA (e.g.,

show abstract

“…where d ij represents the distance between any two sample points i and j in the data space, and the formula is as follows [15]:…”

Section: Smart Finance and Accounting Managementmentioning

confidence: 99%

Framework of the Smart Finance and Accounting Management Model under the Artificial Intelligence Perspective

Chen

2021

Mobile Information Systems

View full text Add to dashboard Cite

With the establishment of modern enterprise systems and the emergence of some large-scale enterprise groups formed through asset reorganization, industry alliances, and cross-industry mergers, new requirements have been put forward for the financial management mode of enterprise groups. This article mainly studies the development of the smart accounting management model architecture under the artificial intelligence perspective. On the accounting sharing service platform, the accounting sharing center does not belong to the logistics branch of any region. Through the accounting sharing center, it provides unified and standardized accounting, asset management, currency revenue and expenditure, etc., for the branches of the logistics company. At the same time, in the network environment, the financial and accounting sharing platform is not unilaterally closed. As each branch under unified management, it has the power to feedback, suggest, and supervise the financial and accounting sharing platform. When testing, one only needs to focus on the external properties of the program, without considering the internal logical structure and internal characteristics of the program at all. The system under test is regarded as a sealed black box, and the boundary value analysis method, the equivalence class division method, and causality are used. Technical methods such as the graph method and error speculation method are tested at the system interface to detect whether the function of each test can receive and output the results correctly. The implementation of the accounting management model of accounting shared services must ensure that the evaluation results of related work are objective and accurate. It is necessary to ensure that the evaluation index system has a high degree of quantification. In the specific scoring, the full score system can be used to score points by experts to ensure the objectivity of the evaluation results and accuracy while improving the operability and enforceability of the evaluation work. The average value of the operating net profit margin is 0.088731, and the median value is 0.082263. The results show that artificial intelligence technology has greatly promoted the development of the smart accounting management model architecture.

show abstract

Artificial intelligence for identifying hydrologically homogeneous regions: A state‐of‐the‐art regional flood frequency analysis

Cited by 28 publications

References 48 publications

College Students’ Mental Health Support Based on Fuzzy Clustering Algorithm

College Students’ Mental Health Support Based on Fuzzy Clustering Algorithm

Fuzzy Ensemble Clustering Approach to Address Regionalization Uncertainties in Flood Frequency Analysis

Framework of the Smart Finance and Accounting Management Model under the Artificial Intelligence Perspective

Contact Info

Product

Resources

About