Regionalization of rainfall characteristics in India incorporating climatic variables and using self-organizing maps

Mannan, Afhaam; Chaudhary, Shushobhit; Dhanya, C. T.; Swamy, Aravind Krishna

doi:10.1080/09715010.2017.1400409

Cited by 22 publications

(14 citation statements)

References 24 publications

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“…Apart from the abovementioned techniques, self-organizing maps (SOMs) are a promising technique for cluster analysis [24] [25]. In this study, cluster analysis involved the use of SOMs to cluster rain gauges in groups (regions) such that the comparability of rain gauges within a region is augmented while the similitude of those between regions is limited.…”

Section: Introductionmentioning

confidence: 99%

Precipitation Regionalization Using Self-Organizing Maps for Mumbai City, India

Parchure¹,

Gedam²

2018

JWARP

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The detailed analysis of individual rain events characteristics is an essential step for improving our understanding of variation in precipitation over different topographies. In this study, the homogeneity among rain gauges was investigated using the concept of "rain event properties," linking them to the main atmospheric system that affects the rainfall in the region. For this, eight properties of more than 23,000 rain events recorded at 47 meteorological stations in Mumbai, India, were analyzed utilizing seasonal (June-September) rainfall records over 2006-2016. The high similarities among the properties indicated the similarities among the rain gauges. Furthermore, similar rain gauges were distinguished, investigated and characterized by cluster analysis using self-organizing maps (SOM). The cluster analysis results show six clusters of similarly behaving rain gauges, where each cluster addresses one isolated class of variables for the rain gauge. Additionally, the clusters confirm the spatial variation of rainfall caused by the complex topography of Mumbai, comprising the flatland near the Arabian Sea, high-rise buildings (urban area) and mountain and hills areas (Sanjay Gandhi National Park located in the northern part of Mumbai).

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Section: Introductionmentioning

confidence: 99%

Precipitation Regionalization Using Self-Organizing Maps for Mumbai City, India

Parchure¹,

Gedam²

2018

JWARP

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“…The self-organizing map (SOM) was developed by Kohonen [23] and is an unsupervised artificial system that can emulate certain mappings that occur in the human brain via competitive learning. The method employed can be divided into the following steps:…”

Section: Self-organizing Map Methodologymentioning

confidence: 99%

“…The self-organizing map (SOM) is an unsupervised neural network methodology that can project high-dimensional input data onto a low dimensional space. Due to the robust clustering function of the SOM, it has been successfully applied in the partitioning of precipitation, hydrology, and landscape [8,23,24]. However, although SOM is not a new method, it has barely been applied in SWCP.…”

Section: Introductionmentioning

confidence: 99%

Self-Organizing Map Network-Based Soil and Water Conservation Partitioning for Small Watersheds: Case Study Conducted in Xiaoyang Watershed, China

Wang

et al. 2020

Sustainability

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Soil and water conservation partitioning (SWCP) considers complex environmental statutes and development demands and serves as a scientific basis for conducting soil erosion management and practice. However, few studies have researched partitioning in small watersheds (< 50 km2), and guidelines for enabling region-specific measures are lacking. In this study, the Xiaoyang watershed located in the red soil region of southern China was selected as a representative small watershed in which to conduct partitioning. The pressure–state–response (PSR) model was used as a framework for establishing an indicator system that included soil erosion sensitivity, the soil erosion condition, and ecosystem services. With three soil and water conservation variables as the input layer, a one-dimensional self-organizing map was applied to identify clusters in the small watershed. The silhouette width was evaluated to determine the optimal number of regions. Based on the associated results, the Xiaoyang watershed was divided into five regions accounting for 82%, 9%, 8%, 2%, and 1% of the total area, respectively. This study provides a framework on which region-specific soil erosion measures can be planned, and it also provides a partitioning method that can be employed in other areas.

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“…Several studies [27,[46][47][48][49][50] have provided an extensive and systematic comparison of CVIs to derive the optimal number of partitions in various datasets obtained from computational experiments, benchmark synthetic data, and real case examples. The most commonly used CVIs in delineation of precipitation regions [10,51,52] are Dunn's index, Davies-Bouldin index, Calinski-Harabasz index, c-index, Dunn Generalized index, Silhoutte index, and Xie-Beni index.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the studies in hydroclimatology have focused on regionalization using various clustering algorithms and/or its performance based on the selection of the attributes. However, limited studies reported the effect of CVI on the formation of homogeneous regions [9,10,51,52]. It is indicated that the CVIs do not result in a single ideal number of clusters [27,48,49] and thereby, it is envisaged that the selection of CVI plays a significant role in the delineation of precipitation regions.…”

Section: Introductionmentioning

confidence: 99%

Role of Cluster Validity Indices in Delineation of Precipitation Regions

Bhatia

Sojan

Simonović́

et al. 2020

Water

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The delineation of precipitation regions is to identify homogeneous zones in which the characteristics of the process are statistically similar. The regionalization process has three main components: (i) delineation of regions using clustering algorithms, (ii) determining the optimal number of regions using cluster validity indices (CVIs), and (iii) validation of regions for homogeneity using L-moments ratio test. The identification of the optimal number of clusters will significantly affect the homogeneity of the regions. The objective of this study is to investigate the performance of the various CVIs in identifying the optimal number of clusters, which maximizes the homogeneity of the precipitation regions. The k-means clustering algorithm is adopted to delineate the regions using location-based attributes for two large areas from Canada, namely, the Prairies and the Great Lakes-St Lawrence lowlands (GL-SL) region. The seasonal precipitation data for 55 years (1951–2005) is derived using high-resolution ANUSPLIN gridded point data for Canada. The results indicate that the optimal number of clusters and the regional homogeneity depends on the CVI adopted. Among 42 cluster indices considered, 15 of them outperform in identifying the homogeneous precipitation regions. The Dunn, D e t _ r a t i o and Trace( W − 1 B ) indices found to be the best for all seasons in both the regions.

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Regionalization of rainfall characteristics in India incorporating climatic variables and using self-organizing maps

Cited by 22 publications

References 24 publications

Precipitation Regionalization Using Self-Organizing Maps for Mumbai City, India

Precipitation Regionalization Using Self-Organizing Maps for Mumbai City, India

Self-Organizing Map Network-Based Soil and Water Conservation Partitioning for Small Watersheds: Case Study Conducted in Xiaoyang Watershed, China

Role of Cluster Validity Indices in Delineation of Precipitation Regions

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