Assessment of regional environmental quality using cluster analysis

Krishnan, Sruthi; C, Mohammed Firoz

doi:10.1016/j.envdev.2023.100832

Cited by 11 publications

(9 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cluster analysis was applied to partition multivariate observations into several meaningful multivariate homogeneous groups or centres called centroids [132,133]. Due to the complex nature of regional geochemical data [134,135], appropriate data transformations must be performed before performing a cluster analysis.…”

Section: Data Processingmentioning

confidence: 99%

Investigations of Chemical Element Distributions in Soil, North Macedonia—A Review

Stafilov,

Šajn,

Alijagić

2024

Minerals

View full text Add to dashboard Cite

This review article considers the following aspects: naturally distributed chemical elements and their enrichments, and the increased occurrence of PTEs due to anthropogenic and urban activities, as well as due to the geochemical uniqueness of certain geochemical landscapes, depending on the lithological environment. The review article is the result of many years of successful cooperation between the Geological Survey of Slovenia and the Faculty of Natural Sciences in Skopje, Ss. Cyril and Methodius University in Skopje, as well as several other institutions from North Macedonia, Russia and Romania but, also, through the voluntary and enthusiastic work of Prof. Trajče Stafilov’s PhD and Master’s students. To create the Geochemical Atlas, the territory of North Macedonia was covered with 995 sampling locations, but 16 separate areas with soil contamination were additionally sampled. The total sum of all collected soil samples was 3983 from 2449 different sampling sites in the period from 2006 to 2017. The analyses were performed at the Institute of Chemistry, Faculty of Natural Sciences in Skopje, at the Ss. Cyril and Methodius University in Skopje, North Macedonia, at the Research Institute for Analytical Instrumentation (ICIA), Cluj-Napoca, Romania, at the Joint Institute for Nuclear Research in Dubna, Moscow Region, Russia, and at Acme Labs in Vancouver, Canada. The sum of all analysed soil samples in all four mentioned laboratories was 7991 from 2006 to 2017. Using advanced mathematical methods such as multivariate statistical methods (HCA, FA, PCA) and artificial neural networks–multilayer perceptron (ANN-MP), predictions were made about the concentrations of potentially toxic elements (PTEs) and their distribution in real space. In less than two decades (2007–2023) of fruitful collaboration, a large number of scientific works have been published: 188 scientific publications, 8 geochemical atlases and 23 chapters in monographs.

show abstract

Section: Data Processingmentioning

confidence: 99%

Investigations of Chemical Element Distributions in Soil, North Macedonia—A Review

Stafilov,

Šajn,

Alijagić

2024

Minerals

View full text Add to dashboard Cite

show abstract

“…The current research proposes a 3-tier assessment methodology for various spatial dimensions at macro, meso, and micro scales through the cluster analysis process. The result of this study is the formulation of recommendations for an appropriate regional development planning framework [21].…”

Section: Literature Reviewmentioning

confidence: 99%

Determining rural development priorities using a hybrid clustering approach: a case study of South Sulawesi, Indonesia

2023

IJATEE

View full text Add to dashboard Cite

South Sulawesi province implements a village assistance program. The program aims to ensure the successful implementation of village development activities, reduce urbanization, and address development problems in rural areas. This research discusses finding alternative solutions to determining the priority status of villages that need assistance from experts. Village assistance is an activity in the context of developing self-sufficiency and feasibility of

show abstract

“…When the main topic can be subdivided into several interrelated parts, cluster analysis allows us to study and characterize the relationship among the different sections of the main topic: how the IoT connects to machine learning and deep learning [34].…”

Section: Cluster Definitionmentioning

confidence: 99%

“…The first is that between 2000 and 2014, the number of publications remained relatively the same, not exceeding 100 per year. The second is that between 2015 and 2018, the number of publications on "smart agriculture" increased significantly, from 155 in 2015 to 936 in 2019, an average annual growth of approximately 200; this may be because, with the emergence of digitalization, the Industry 4.0 model has been extended to the agricultural sector, significantly promoting the development of smart agriculture [34,35]. The third is 2020 and 2021.…”

Section: Analysis Of the Trendsmentioning

confidence: 99%

“…This study selected three parameters commonly used in statistics to characterize phrases' evolution over time objectively [34]. The first parameter is the average slope of fitting the number of publications per year; this parameter represents the efficiency of first-order linear fitting of the curve between the number of high-frequency phrases and years.…”

Section: Smart Agriculturementioning

confidence: 99%

See 1 more Smart Citation

Bibliometric Analysis of Trends in Smart Irrigation for Smart Agriculture

Pang,

Marinello,

Tang

et al. 2023

Sustainability

View full text Add to dashboard Cite

Agriculture is considered one of the most critical sectors that play a strategic role in ensuring food security. It is directly related to human development and social stability. The agricultural sector is currently incorporating new technologies from other areas. These phenomena are smart agriculture and smart irrigation. However, a challenge to research is the integration of technologies from different knowledge fields, which has caused theoretical and practical difficulties. Thus, our purpose in this study has been to understand the core of these two themes. We extracted publications in Scopus and used bibliometric methods for high-frequency word and phrase analysis. Research shows that current research on smart agriculture mainly focuses on the Internet of Things, climate change, machine learning, precision agriculture and wireless sensor networks. Simultaneously, the Internet of Things, irrigation systems, soil moisture, wireless sensor networks and climate change have received the most scholarly attention in smart irrigation. This study used cluster analysis to find that the IoT has the most apparent growth rate in smart agriculture and smart irrigation, with five-year growth rates of 1617% and 2285%, respectively. In addition, machine learning, deep learning and neural networks have enormous potential in smart irrigation compared with smart agriculture.

show abstract

Assessment of regional environmental quality using cluster analysis

Cited by 11 publications

References 35 publications

Investigations of Chemical Element Distributions in Soil, North Macedonia—A Review

Investigations of Chemical Element Distributions in Soil, North Macedonia—A Review

Determining rural development priorities using a hybrid clustering approach: a case study of South Sulawesi, Indonesia

Bibliometric Analysis of Trends in Smart Irrigation for Smart Agriculture

Contact Info

Product

Resources

About