Spatial homogeneity of extreme precipitation indices using fuzzy clustering over northeast India

Goyal, Manish Kumar; Gupta, Shivam; Sarma, Arup Kumar

doi:10.1007/s11069-019-03715-z

Cited by 12 publications

(10 citation statements)

References 37 publications

(34 reference statements)

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“…This study is consistent with the method used in our study to reflect changes in water quality factor concentrations through cluster analysis. In the precipitation study at Northeast India, Goyal, Shivam, and Sarma (2019) used fuzzy clustering to cluster the selected sites based on the six parameters of these precipitation indices, namely, latitude, longitude, mean, standard deviation, minimum and maximum (Goyal et al, 2019), and finally divided the sampling points into five categories for analysis, which is consistent with the method of classification analysis of sampling points in our paper. In the study of fuzzy clustering, Mao et al (2019) used the density centre as the initial cluster centre and finally developed a typical point selection method based on sampling density centre selection, which is consistent with our principles of fuzzy clustering in the study area and the selection of typical points based on the centre points of the clustering results.…”

Section: Discussionsupporting

confidence: 59%

“…In the precipitation study at Northeast India, Goyal, Shivam, and Sarma (2019) used fuzzy clustering to cluster the selected sites based on the six parameters of F I G U R E 5 Food web structure level index changes of J1, J23, J24 and J32 (a-d represent J1, J23, J24 and J32, respectively, and the time is predicted based on the month of rainfall data collection. The red line in the figure is the mean line) these precipitation indices, namely, latitude, longitude, mean, standard deviation, minimum and maximum (Goyal et al, 2019), and finally divided the sampling points into five categories for analysis, which is consistent with the method of classification analysis of sampling points in our paper. In the study of fuzzy clustering, Mao et al (2019) used the density centre as the initial cluster centre and finally devel-…”

Section: Fuzzy Clustering To Determine Typical Pointsmentioning

confidence: 99%

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Effects and prediction of nonpoint source pollution on the structure of aquatic food webs

et al. 2020

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Nonpoint source pollution entering rivers will pollute water quality, degrading the health of aquatic ecosystems. However, owing to the lack of quantitative research on the effects of nonpoint source pollution on the structure of aquatic food webs, there is a lack of quantitative basis for river management. Nonpoint source pollution is not only difficult to control effectively, but also the success rate of water ecological restoration projects is low. With the increasing proportion of nonpoint source pollution in water environmental problems, it is urgent to quantitatively assess and predict the impact of nonpoint source pollution on the structure of food webs. Therefore, this thesis presents a method for quantitatively assessing and predicting the impact of nonpoint source pollution on the structure of food webs through using fuzzy clustering to screen the typical points of the impact of nonpoint source pollution, then using canonical correspondence analysis (CCA) and partial least squares regression analysis to comprehensively filtrate the driving factors affect food web that results in nonpoint source pollution, and then determining the impact of each driving factor on the structure of food webs. Finally, the change trend of food web structure is predicted. The results show that (1) the driving factors that the nonpoint source pollution that affects the food web structure is NH3‐N and chemical oxygen demand (COD). The increase in NH3‐N and COD promotes the growth of phytoplankton, causing the change of the primary productivity of the ecosystem, and ultimately changes the entire food web structure; (2) NH3‐N and COD affect the stability, maturity, connectivity and complexity of the aquatic food web structure. The increase of NH3‐N increases the connectivity and maturity of the food web structure but reduces complexity and stability; the increase of COD increases the connection of the food web structure, while reducing the other three indicators; (3) in some areas with good water quality, aquatic species diversity is high, the relationship of interspecies dietary is complex, food web structure level index is high and the structure of food web is stable. The food web structure in the rainy season will be better than that in the dry season. In some areas with severe water pollution and poor food web structure, the ability of the food web to resist external interference is weak. The food web structure in the rainy season will be worse than that in the dry season owing to rainfall into the river. The methods and conclusions in this treatise can provide a reliable and quantitative scientific basis for river ecosystem management and ecosystem restoration and can improve the success rate of ecological restoration projects.

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Section: Discussionsupporting

confidence: 59%

Section: Fuzzy Clustering To Determine Typical Pointsmentioning

confidence: 99%

Effects and prediction of nonpoint source pollution on the structure of aquatic food webs

et al. 2020

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“…3). Regionalization on the basis of the properties of hydro-meteorological data helps in identifying the regions re ecting the similar characteristics which could be useful in designing hydrological structures as well as planning and management of water resources of the region (Goyal et al, 2019). Clustering result showed relevance to the physiographic aspects that characterize the rainfall dynamics in CRJ.…”

Section: Resultsmentioning

confidence: 98%

“…Floods represent about one-third of all natural disasters. Together with storms they comprise 77% of economic losses caused by extreme weather events in Europe (Lechowska, 2018).The city of São Paulo, home to 11 million people, suffers constantly the effects of ooding caused by extreme precipitation Haddad and Teixeira (2015) estimated that oods contributed to reduce city growth and residents' welfare, as well as hampering local competitiveness in both domestic and international markets. An intra-city total impact-damage ratio of 2.2 and an economy-wide total impactdamage ratio of 5.0 were found.…”

Section: Resultsmentioning

confidence: 99%

Homogeneous regions for rainfall distribution in the city of Rio de Janeiro associated with the risk of natural disasters

2021

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Understanding the occurrence of natural disasters in regions where the occurrence is high is very important, it is known that the occurrence of disasters associated with intense rains are a source of research in different locations around the globe, being important not only for increasing accuracy of weather forecasting models, but important information for civil defense, where lives can be saved. The increase in the occurrence of natural disasters related to extreme rainfalls has become a problem of large urban centers, such as the city of Rio de Janeiro (CRJ). Thus, the identi cation of homogeneous regions for rainfall distribution (HRRD) becomes essential to identify regions at risks of oods and mass movements. The aim of this research was to identify HRRD in CRJ associated to the risk of natural disasters. The identi cation of homogeneous regions was carried out with the use of monthly rainfall data from 14 pluviometric stations spatially distributed in the study area between 1997 and 2018.Rainfall data were submitted to descriptive statistical analysis, and subsequently to Cluster Analysis.Cluster analysis identi ed 4 homogeneous groups regarding annual rainfall distribution. The result showed relevance regarding physiographic aspects that characterize the rainfall dynamics in CRJ, highlighting areas favorable to the occurrence of natural disasters.

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“…Modarres (2006) and Ahmad et al (2013) respectively identified the homogeneous precipitation regions of Iran and Malaysia using a hierarchical clustering approach. Sahin and Cigizoglu (2010), Goyal et al 2019, andBasalirwa (1995) respectively in Turkey, India, and Uganda also applied clustering approaches for the same purpose. Furthermore, Abadi et al (2019) applied a multivariate clustering approach to divide Bolivia into identical and coherent climatological sub-regions.…”

Section: Introductionmentioning

confidence: 99%

Redefining Homogeneous Climate Regions in Bangladesh Using Multivariate Clustering Approaches

Mahmud

Sumana

Maqbool

et al. 2021

Preprint

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The knowledge of the climate pattern for a particular region is important to alleviate the impact of climate change and protect the environment by taking appropriate actions based on geographical knowledge. It is also equally important for water resources planning and management. In this study, the regional disparities and similarities have been revealed among different climate stations or regions in Bangladesh based on different climatological factors such as rainfall, temperatures, relative humidity, sea level pressure, cloud cover, wind speed, the sunshine hour. We have selected one of the best-fitted algorithms for particular climate data from three multivariate clustering approaches named hierarchical clustering, partitioning around medoids (PAM), and K-means clustering by using different validation tests. Four homogeneity tests (Mann-Kendall Test, Pettitt's test, Buishand Range Test, Standard Normal Homogeneity Test) also have been performed for each of the clusters created based on several factors. The results suggest that the climate regions or meteorological stations of Bangladesh can be clustered into two groups based on a combination of climatological variables. According to the findings, there is a huge variation between the two groups in terms of climatological factors. The first group (cluster 1) is located in the northern part of the country that includes drought-prone and vulnerable regions, whereas, the second group (cluster 2) contains rain-prone and hilly regions, which are mostly situated in the southern part. All newly defined clusters show homogeneous behavior with few exceptions such as clusters based on sea level pressure are not homogeneous.

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Spatial homogeneity of extreme precipitation indices using fuzzy clustering over northeast India

Cited by 12 publications

References 37 publications

Effects and prediction of nonpoint source pollution on the structure of aquatic food webs

Effects and prediction of nonpoint source pollution on the structure of aquatic food webs

Homogeneous regions for rainfall distribution in the city of Rio de Janeiro associated with the risk of natural disasters

Redefining Homogeneous Climate Regions in Bangladesh Using Multivariate Clustering Approaches

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