scholarly journals Temporal and spatial variations of extreme precipitation in the Guangdong-Hong Kong-Macao Greater Bay area from 1961 to 2018

2021 ◽  
Author(s):  
Yueying Zhou ◽  
Zhijian Luo ◽  
Shenlin Li ◽  
Zufa Liu ◽  
Yanpeng Shen ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Hong Kong ◽  
Extreme Precipitation ◽  
Negative Impact ◽  
Moving Average ◽  
Principal Component ◽  
Kriging Interpolation ◽  
Mean Values ◽  
Bay Area ◽  
Temporal And Spatial

Abstract Disasters caused by extreme precipitation under global warming are anticipated to have a strong negative impact on urban construction and social security. In this study, daily grid precipitation datasets of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) for the period 1961–2018 were extracted to explore the temporal and spatial characteristics of extreme precipitation by using regression analysis, moving average and kriging interpolation. The frequency and intensity indices showed an increasing trend, whereas a decreasing trend was found for the persistence indices, which indicates that GBA tends to slowly become wetter. The mean values of extreme precipitation indices (EPIs) in GBA generally increased from west to east and from north to south. Except for the indices of consecutive wet days and consecutive dry days, other EPIs showed an upward trend in most regions, especially in coastal cities where floods are more likely to occur. Principal component analysis and regression analysis showed that the correlations between the EPIs mostly passed the 0.05 significance test, which suggests that they had a good indicator of extreme precipitation in GBA. This study provides a theoretical basis for extreme precipitation disaster prevention and control within the urban agglomerations of the GBA.

Impacts of urbanization on extreme precipitation in the Guangdong-Hong Kong-Macau Greater Bay Area

Urban Climate ◽  
2021 ◽  
Vol 38 ◽  
pp. 100904
Author(s):  
Yanni Li ◽  
Weiwen Wang ◽  
Ming Chang ◽  
Xuemei Wang
Keyword(s):  
Hong Kong ◽  
Extreme Precipitation ◽  
Bay Area

scholarly journals Quality Assessment of Ground Water in Dhamar City, Yemen

2018 ◽  
Vol 6 (4) ◽  
pp. 56-71
Author(s):  
Hefdallah Al Aizari ◽  
Ahamed Lebkiri ◽  
Mhammde Fadli ◽  
Saeed S. Albaseer
Keyword(s):  
Drinking Water ◽  
Regression Analysis ◽  
Ground Water ◽  
Principal Component ◽  
Total Hardness ◽  
Sodium Absorption ◽  
Physical Parameters ◽  
Statistical Regression ◽  
Chemical Parameters ◽  
Mean Values

Chemical and statistical regression analysis on groundwater at five fields (17 sampling wells) located in Dhamar city, the central highlands of Yemen, was carried out. Samples were collected from the ground water supplies (tube wells) during the year 2015. Physical parameters studied include (values between bracket s represents the measured mean values) temperature (T, 25°), total dissolved solids (TDS, 271.47), pH (7.5), and electrical conductivity (EC, 424.18). The chemical parameters investigated include total hardness (TH, 127.45), calcium (Ca2+, 32.89), magnesium (Mg2+, 11.03), bicarbonate (HCO3̶, 143.84), sulphate (SO42-, 143.84), sodium (Na+, 35.11), potassium (K+, 6.28) and Chloride (Cl ̵, 22.69). The results were compared with drinking water quality standards issued by Yemen standards for drinking water. Except for T° and pH, all other measured parameters fall below the minimum permissible limits. The correlation between various physio-chemical parameters of the studied water wells was performed using Principal Component Analysis (PCA) method. The obtained results show that all water samples are potable and can be safely used for both drinking and irrigation purposes. This comes in agreement with the public notion about groundwater of Dhamar Governorate. Sodium Absorption Ratio (SAR) values were calculated and found below 3 except for one drill. The results revealed that systematic calculations of correlation coefficients between water parameters and regression analysis provide a useful means for rapid monitoring of water quality.International Journal of EnvironmentVolume-6, Issue-4, Sep-Nov 2017, page: 56-71

scholarly journals The Influence of Financial Agglomeration on the Export Trade in the Guangdong-Hong Kong-Macao Greater Bay Area: An Analysis Based on Mediation Effect of Technological Innovation

2021 ◽  
Vol 8 (3) ◽  
pp. 31
Author(s):  
Zhisen Zeng ◽  
Huixian Zeng ◽  
Jingyu Wu
Keyword(s):  
Regression Analysis ◽  
Hong Kong ◽  
Technological Innovation ◽  
Mediation Effect ◽  
Export Trade ◽  
Mediation Model ◽  
Bay Area ◽  
Mediator Variable

So far, the relation between financial agglomeration and export trade is complex and there are few related studies. However, research on this topic will be of great value to the development of the Guangdong-Hong Kong-Macau Greater Bay Area (the Greater Bay Area). This paper aims to use the mediation model to analyze the role of technological innovation as an mediator variable between financial agglomeration and export trade. Based on the relevant data of the Greater Bay Area from 2009 to 2018, regression analysis was performed using the three equations of the mediation model. The mediator variable was then replaced to conduct a robustness test, and it was found that there is indeed an mediation effect; technological innovation acts as an mediator variable between financial agglomeration and export trade. Therefore, it can be concluded that the financial agglomeration in the Greater Bay Area can effectively promote technological innovation, while technological innovation will inhibit exports to a certain extent.

Neighbourhood attachment and satisfaction: a Hong Kong’s case study

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Adrienne La Grange ◽  
Yung Yau
Keyword(s):  
Regression Analysis ◽  
Hong Kong ◽  
Physical Environment ◽  
Principal Component ◽  
High Density ◽  
Hierarchical Regression ◽  
Hierarchical Regression Analysis ◽  
Content Type ◽  
High Density Housing

Purpose This paper aims to study neighbourhood attachment and satisfaction in a middle-class, high-density and semi-gated neighbourhood in Hong Kong. Design/methodology/approach Drawing on the findings of survey on 356 households, a principal component analysis and hierarchical regression analysis were conducted to assess how attachment and satisfaction were manifested and whether they were manifested as separate phenomena. Findings Attachment and satisfaction in neighbourhoods were manifested as separate phenomena. It was further found that residents were broadly attached to and satisfied with their neighbourhood. Of the neighbourhood characteristics identified as influencing satisfaction in previous research, the support was found only for the physical environment and safety but concluded that satisfaction was also influenced by status, neighbourhood youths’ ambition and schools. Contrary to the expectation, the authors did not find support for deeper social bonds as an element of satisfaction. The hierarchical regression analysis indicated that satisfaction may lead to increased attachment. Social implications This study offers policymakers and housing managers’ valuable insights into the management of increasingly large and complex residential neighbourhoods. It helps us understand which initiatives are likely to lead to greater attachment. Originality/value Previous studies have focused on neighbourhood attachment and satisfaction in typical low/medium-density settings. This study extends previous efforts to a high-density housing estate of Hong Kong.

Monthly Maximum Accumulated Precipitation Forecasting Using Local Precipitation Data and Global Climate Modes

2014 ◽  
Vol 18 (6) ◽  
pp. 999-1006
Author(s):  
Junaida Binti Sulaiman ◽  
◽  
Herdianti Darwis ◽  
Hideo Hirose
Keyword(s):  
Regression Analysis ◽  
Extreme Precipitation ◽  
Southern Oscillation ◽  
Global Climate ◽  
Moving Average ◽  
Climate Indices ◽  
Precipitation Data ◽  
Out Of Sample ◽  
Sample Test ◽  
High Precipitation Events

Successive days of precipitation are known to cause flooding in monsoon-susceptible countries. Forecasting of daily precipitation facilitates the prediction of the occurrences of rainfall and number of wet days. Using the maximum five-day accumulated precipitation (MX5d), we can predict the magnitude of precipitation in a specific period as it may indicate the extreme precipitation. In this study, a method to forecast monthly extreme precipitation using artificial neural networks (ANNs) is assessed using past MX5d data and global climate indices such as Southern Oscillation Index (SOI), Madden Julian Oscillation (MJO), and Dipole Mode Index (DMI) in Kuantan and Kota Bharu, Malaysia. The use of combined inputs (MX5d with SOI, MJO, and DMI) is proposed to investigate the concurrent effect of lagged values of local precipitation data and global climate indices on seasonal extreme precipitation. Four cases of data are sampled representing two major seasonal variations in Malaysia. The analysis of extreme precipitation trends is important for the prediction of high precipitation events. ANNs are widely applied in the hydrology field because of their nonlinear ability in predicting nonstationary and seasonal data. In this paper, we have compared ANNs with seasonal autoregressive integrated moving average (ARIMA) and regression analysis using out-of-sample test data. The results for Kuantan indicate that seasonal ARIMA is the best method to forecast extreme precipitation when MX5d lags are used as input. For Kota Bharu, ANN exhibits better generalization ability than ARIMA and regression analysis when dual inputs (lagged MX5d and lagged global climate indices) are utilized in the model.

scholarly journals Geospatial Mapping, Environmetrics and Indexing Approach for a Tropical River Sediment in Southern Nigeria

2020 ◽  
Vol 63 (3) ◽  
pp. 176-187
Author(s):  
Osikemekha Anthony Anania ◽  
John Ovie Olomukoro ◽  
Alex Ajeh Enuneku
Keyword(s):  
Heavy Metals ◽  
Ecological Risk ◽  
Anthropogenic Activities ◽  
Risk Index ◽  
Principal Component ◽  
Kriging Interpolation ◽  
Mean Values ◽  
Significant Difference ◽  
The Mean ◽  
Geospatial Mapping

The objectives of this study are to assess the trace and heavy metals pollution in the sediments of Ossiomo river, using geospatial mapping, environmetrics and ecological risk indices. The results from the descriptive statistics showed that there was significant difference (P<0.05) of the mean values of Fe, Mn, Cu, Cr, Cd, Pb, Ni and V. A posterior analysis using Duncan multiple regression analysis showed that stations 2 and 3 were significantly different from stations 1 and 4. While, there was no significant difference (P>0.05) in the mean values of Zn across the stations. The results of the relationship of the metals revealed a negative correlation between Fe and Mn with the other metals correspondingly. The results of the Kriging interpolation indicated a strong bull eye colour for stations 2 and 3 (6.42), while stations 1 and 4 were minimal (1.4). The results of the geospatial mapping indicated Fe, Zn and Mn to be the most dominant metals across the stations. The results of the PCA (principal component analysis) yielded 16 variables under 9 components with Eigenvalues >1 in components 1- 6 and these variables explained 99.99 % of the total variance in the sediment. The results of the degree of suitability and sphericity of the PCA revealed a high significant difference at P<0.001. The results of the potential ecological risk index values were very high in station 2 (824.30) and 3 (802.11) correspondingly. That of index of geo-accumulation was generally low (< 2). The findings from this study generally revealed the source apportionment of the trace and heavy metals to come from anthropogenic influences such as farming; fertilizers. Sustainable agriculture is highly recommended in order to reduce the impacts of anthropogenic activities, deterioration of the ecosystem and possible death of the life forms in this watercourse.  

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