Drivers of water conflicts in co-evolving human-water systems in Cauvery Basin, Southern India

2020 ◽  
Author(s):  
Veena Srinivasan ◽  
Neha Khandekar ◽  
Ganesh Shinde
Keyword(s):  
Principal Component Analysis ◽  
River Basin ◽  
Water Availability ◽  
Print Media ◽  
Principal Component ◽  
Water Systems ◽  
Statistical Analyses ◽  
Southern India ◽  
Water Conflicts ◽  
Cauvery Basin

<div> </div><div> <div>​India is a rapidly evolving economy with rising demands from various sectors and stakeholders including the environment.  Water conflicts emerge when mechanisms to allocate water between different sectors do not keep up with changing demands. </div> <div> </div> <div>Because biophysical drivers of water availability such as industrialization, urbanization, and deforestation are driven by humans - integration of underlying socio-economic drivers with bio-physical in is, therefore, understanding water conflicts requires a socio-hydrological approach.</div> <div> </div> <div>In an attempt to understand this dynamism of human-water interactions within the landscape and improve the emergence of water conflicts, we present the case of the Cauvery basin -- a highly contentious inter-state river basin in Southern India. Over a two-decade period, we explore how catchments have co-evolved by studying signatures of 53 watersheds in Cauvery basin and correlate it to the occurrence of conflict in print media. Using spatiotemporal cluster statistical analyses tools like principal component analysis in R, we explore how changes in the landscape have triggered water conflicts.</div> </div>

Wood anatomical variability in Early Permian ‘araucarioids’

IAWA Journal ◽  
2014 ◽  
Vol 35 (3) ◽  
pp. 307-331 ◽  
Author(s):  
Menno Booi ◽  
Isabel M. van Waveren ◽  
Johanna H.A. van Konijnenburg-van Cittert
Keyword(s):  
Principal Component Analysis ◽  
Wood Species ◽  
Principal Component ◽  
Statistical Analyses ◽  
Late Paleozoic ◽  
Individual Species ◽  
Early Permian ◽  
Multivariate Statistical Analyses ◽  
Multivariate Statistical ◽  
Anatomical Analysis

Although araucarioid wood is poor in diagnostic characters, well in excess of 200 Late Paleozoic species have been described. This study presents a largescale anatomical analysis of this wood type based on the fossil wood collections from the Early Permian Mengkarang Formation of Sumatra, Indonesia. Principal Component Analysis visualisation, in conjunction with uni- and multivariate statistical analyses clearly show the wood from the Mengkarang Formation to be a contiguous micromorphological unit in which no individual species can be distinguished. Pycnoxylic wood species described previously from this collection or other collections from the Mengkarang Formation fall within the larger variability described here. Based on comparison with wood from modern-day Araucariaceae, the Early Permian specimens can be differentiated from extant (but unrelated) “araucarioids” by a few (continuous) characters.

scholarly journals Quantifying the Information Content of a Water Quality Monitoring Network Using Principal Component Analysis: A Case Study of the Freiberger Mulde River Basin, Germany

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 420 ◽  
Author(s):  
Thuy Hoang Nguyen ◽  
Björn Helm ◽  
Hiroshan Hettiarachchi ◽  
Serena Caucci ◽  
Peter Krebs
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
River Basin ◽  
Principal Component ◽  
Water Quality Monitoring ◽  
Component Analysis ◽  
Quality Monitoring ◽  
Critical Parameters ◽  
Spatial And Temporal Variation ◽  
Effectiveness Monitoring

Although river water quality monitoring (WQM) networks play an important role in water management, their effectiveness is rarely evaluated. This study aims to evaluate and optimize water quality variables and monitoring sites to explain the spatial and temporal variation of water quality in rivers, using principal component analysis (PCA). A complex water quality dataset from the Freiberger Mulde (FM) river basin in Saxony, Germany was analyzed that included 23 water quality (WQ) parameters monitored at 151 monitoring sites from 2006 to 2016. The subsequent results showed that the water quality of the FM river basin is mainly impacted by weathering processes, historical mining and industrial activities, agriculture, and municipal discharges. The monitoring of 14 critical parameters including boron, calcium, chloride, potassium, sulphate, total inorganic carbon, fluoride, arsenic, zinc, nickel, temperature, oxygen, total organic carbon, and manganese could explain 75.1% of water quality variability. Both sampling locations and time periods were observed, with the resulting mineral contents varying between locations and the organic and oxygen content differing depending on the time period that was monitored. The monitoring sites that were deemed particularly critical were located in the vicinity of the city of Freiberg; the results for the individual months of July and September were determined to be the most significant. In terms of cost-effectiveness, monitoring more parameters at fewer sites would be a more economical approach than the opposite practice. This study illustrates a simple yet reliable approach to support water managers in identifying the optimum monitoring strategies based on the existing monitoring data, when there is a need to reduce the monitoring costs.

scholarly journals IoT and Engagement in the Ubiquitous Museum

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1387 ◽  
Author(s):  
Roberto Pierdicca ◽  
Manuel Marques-Pita ◽  
Marina Paolanti ◽  
Eva Malinverni
Keyword(s):  
Principal Component Analysis ◽  
Internet Of Things ◽  
Sensor Network ◽  
Interdisciplinary Research ◽  
Physical Space ◽  
Principal Component ◽  
Statistical Analyses ◽  
Data Driven ◽  
Next Generation ◽  
Significant Survival

In increasingly hyper-connected societies, where individuals rely on short and fast online communications to consume information, museums face a significant survival challenge. Collaborations between scientists and museums suggest that the use of the technological framework known as Internet of Things (IoT) will be a key player in tackling this challenge. IoT can be used to gather and analyse visitor generated data, leading to data-driven insights that can fuel novel, adaptive and engaging museum experiences. We used an IoT implementation—a sensor network installed in the physical space of a museum—to look at how single visitors chose to enter and spend time in the different rooms of a curated exhibition. We collected a sparse, non-overlapping dataset of individual visits. Using various statistical analyses, we found that visitor attention span was very short. People visited five out of twenty rooms on average, and spent a median of two minutes in each room. However, the patterns of choice and time spent in rooms were not random. Indeed, they could be described in terms of a set of linearly separable visit patterns we obtained using principal component analysis. These results are encouraging for future interdisciplinary research that seeks to leverage IoT to get numerical proxies for people attention inside the museum, and use this information to fuel the next generation of possible museum interactions. Such interactions will based on rich, non-intrusive and diverse IoT driven conversation, dynamically tailored to visitors.

scholarly journals Chemotypes of Pistacia atlantica Leaf Essential Oils from Algeria

2010 ◽  
Vol 5 (1) ◽  
pp. 1934578X1000500 ◽  
Author(s):  
Nadhir Gourine ◽  
Isabelle Bombarda ◽  
Mohamed Yousfi ◽  
Emile M. Gaydou
Keyword(s):  
Principal Component Analysis ◽  
Essential Oil ◽  
Essential Oils ◽  
Principal Component ◽  
Component Analysis ◽  
Statistical Analyses ◽  
Pistacia Atlantica

The essential oils obtained by hydrodistillation of Pistacia atlantica Desf. leaves collected from different regions of Algeria were analyzed by GC and GC-MS. The essential oil was rich in monoterpenes and oxygenated sesquiterpenes. The major components were α-pinene (0.0-67%), δ-3-carene (0.0-56%), spathulenol (0.5-22%), camphene (0.0-21%), terpinen-4-ol (0.0-16%) and β-pinene (0.0-13%). Among the various components identified, twenty were used for statistical analyses. The result of principal component analysis (PCA) showed the occurrence of three chemotypes: a δ-3-carene chemotype (16.4-56.2%), a terpinen-4-ol chemotype (10.8-16.0%) and an α-pinene/camphene chemotype (10.9-66.6% / 3.8-20.9%). It was found that the essential oil from female plants (δ-3-carene chemotype) could be easily differentiated from the two other chemotypes corresponding to male trees.

scholarly journals Typology of Medium-Sized Cities in Spain Using PCA (Principal Component Analysis)

2021 ◽  
Vol 13 (5) ◽  
pp. 2456
Author(s):  
Francisco Cebrián-Abellán ◽  
María-Jesús González-González ◽  
María-Eva Vallejo-Pascual
Keyword(s):  
Principal Component Analysis ◽  
Economic Activity ◽  
Statistical Data ◽  
Principal Component ◽  
Statistical Analyses ◽  
The Other ◽  
Processes Of Change ◽  
Cultural Contexts ◽  
The One ◽  
Different Sources

This article analyses processes of change undergone by Spanish medium-sized cities during 1981–2011 on the one hand, and 2000–2018 on the other, as they are different sources. We established a classification to show the importance of this type of city starting from the hypothesis that the process is a generalised one in which they behave according to their position in the territory. The dynamics of change are predominantly associated with contexts of economic expansion. The typology was generated based on population and housing variables, which synthesise the role played by economic activity in each city. Additional methodologies were used: firstly, the bibliography on medium-sized cities in different social and cultural contexts was reviewed; secondly, statistical data were selected, compiled and processed using multivariant statistical analyses, and the results mapped. A study of 133 cities was carried out and absolute values and variation rates used to understand the processes of change. As a result, seven representative groups were obtained. The conclusions of the study can be corroborated by different sources.

Use of PCA-RBF model for prediction of chlorophyll-a in Yuqiao Reservoir in the Haihe River Basin, China

2013 ◽  
Vol 14 (1) ◽  
pp. 73-80 ◽  
Author(s):  
Liu Xiaobo ◽  
Dong Fei ◽  
He Guojian ◽  
Liu Jingling
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Chlorophyll A ◽  
River Basin ◽  
Forecast Error ◽  
Principal Component ◽  
Haihe River Basin ◽  
Haihe River ◽  
Yuqiao Reservoir ◽  
The Haihe River Basin

Chlorophyll-a is a well-accepted index for phytoplankton abundance and population of primary producers in an aquatic environment. The relationships between chlorophyll-a and 18 chemical, physical and biological water quality variables in YuQiao Reservoir (YQR) in the Haihe River Basin in P.R. China were studied by using principal component analysis (PCA) coupled with a radial basis function network (RBF) model to predict chlorophyll-a levels. Principal component analysis was used to simplify the complexity of relations between water quality variables. Score values obtained by PC scores were used as independent variables in the RBF models. In the forecast, only five selected score values obtained by PC analysis were used for the prediction of chlorophyll-a levels. Correlative analysis between the modeled results and observed data indicates that the correlative coefficient is 0.61, and analysis of the forecast error rate shows that the average forecast error is 32.9%, proving the viability of the forecast model.

scholarly journals New Approach in Analyzing Risk Level of Flood in Tropical Region: A Case Study at Pahang River Basin, Malaysia

2018 ◽  
Vol 7 (4.34) ◽  
pp. 103
Author(s):  
Aqilah Ismail ◽  
Ahmad Shakir Mohd Saudi ◽  
Mohd Khairul Amri Kamarudin ◽  
Muhammad Hafiz Md Saad ◽  
Azman Azid ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Water Level ◽  
River Basin ◽  
Flood Risk ◽  
Stream Flow ◽  
Principal Component ◽  
Component Analysis ◽  
Suspended Solid ◽  
Control Limit ◽  
Risk Level

This study focuses on flood risk recognition factor that leads to major contribution of floods in Pahang River basin, identify the correlation between variables and determine factor that influence the flood risk pattern in Pahang. Four hydrological variables been applied. Chemometric technique of Principal Component Analysis (PCA) method and Statistical Process Control (SPC) method were being applied to identify the main contributor for flood, predicting hydrological modeling and risk of flood occurrence at Pahang river basin. Findings from Principal Component Analysis (PCA) confirmed that all selected variables were significant. The relationship between Suspended Solid and Stream Flow with Water Level were very high with correlation of coefficient value more than 0.7. SPC set up a new control limit for all variables. Data beyond the Upper Control Limit (UCL) value is considered as high risk for flood occurrence. Most of the trend pattern showed in year 2007 as high peak. Rapid development growth and anthropogenic activities caused the sediment of Suspended Solid triggered the Water Level and Stream Flow to arise than normal level. Thus, local authority should take earlier precaution for flood prevention and emergency responses plan at the study area for any development of land by takes obligatory action to the developers especially those development that arise along river channel. 

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