Analysis of Factors Affecting the Coordinated Development of Urbanization and the Ecological Resource Environment in Southwest China Based on Data Mining

2021 ◽  
Vol 147 (3) ◽  
pp. 04021034
Author(s):  
Su He ◽  
Dejiang Luo ◽  
Ke Guo
Keyword(s):  
Data Mining ◽  
Southwest China ◽  
Coordinated Development ◽  
Factors Affecting ◽  
Resource Environment

Scenario Analysis on the Water Environment Carrying Capacity of Wuliangsuhai Lake Basin

2012 ◽  
Vol 616-618 ◽  
pp. 1388-1393
Author(s):  
Hong Jun Lei ◽  
Lina Yin ◽  
Bei Dou Xi
Keyword(s):  
Sustainable Development ◽  
Carrying Capacity ◽  
Water Environment ◽  
Social Economy ◽  
Lake Basin ◽  
Environmental Capacity ◽  
Coordinated Development ◽  
The Sustainable Development ◽  
Capacity Index ◽  
Resource Environment

Rapid social-economic development and the ever-increasing water pollutant discharge of Wuliangsuhai lake basin made the water quality deteriorated; the lake eutrophication intensified and heavily restricted the sustainable development of the watershed’s social economy. Wuliangsuhai lake basin was selected as the study area. By the use of system dynamics simulation model the water environment and social economy were connected, and three kinds of improved scenarios (resource-environment constrained type, social economy constrained type, coordinated development type) were proposed based on the inertial and planning schemes. Ten indexes of water environment carrying capacity, i.e. the GDP per capita, ratio of industrial output to GDP, et al., were selected, and fuzzy comprehensive evaluation method was used. Results showed that, the water environmental capacity utilization ratios of COD and TN in the resource-environment constrained scenario were 69.5% and 57.9% in 2020, respectively. And the water environment carrying capacity index increased from 0.19 in 2008 to 0.31 in 2020. Under the coordinated development scenario, the surplus environmental capacity of COD and TN were 1755.46t/a, 260.21t/a respectively, and the water environment carrying capacity index was up to 0.29 in 2020. The sustainable development plan was proposed and some feasible suggestions and strategies were put forward.

scholarly journals Applying Improved Apriori Algorithm in Figuring out the Relation between Weather Factors and Rainfall

2020 ◽  
Vol 1 (1) ◽  
pp. 23-26
Author(s):  
Siti Zulaikha ◽  
Martaleli Bettiza ◽  
Nola Ritha
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Light Intensity ◽  
Apriori Algorithm ◽  
Weather Factors ◽  
Factors Affecting ◽  
Data Store ◽  
Repeated Pattern ◽  
Major Factors ◽  
Sun Light

Data on the rainfall is compelling to study as it becomes one of the major factors affecting the weather in a certain region and various aspects of life as well. Generally, predicting rainfall is performed by analyzing data in the past in certain methods. Rainfall is prone to follow repeated pattern in sequence of time. The utilization of big data mining is expected to result in any valuable information that used to be unrevealed in the big data store. Some methods used in data mining are Apriori Algorithm and Improved Apriori Algorithm. Improved Apriori itself is to represent the database in the form of matrix to describe its relation in the database. Data used in this research is the rainfall factor in 2016 in Tanjungpinang city. Based on the test of Improved Apriori Algorithm, it was found out that the relation of the rainfall and weather factors utilizing 2 item sets, that is, if the temperature is low (24,0 - 26,0), the humidity is high (85 - 100), then the rainfall is mild. If the temperature is low (24,0 - 26,0), the light intensity is low (0 – 3), then the rainfall is heavy, and 3 item sets if the temperature is low (24,0 - 26,0), the humidity is high (85 - 100), the sun light intensity is low (0-3), then the rainfall is medium.

scholarly journals FACTORS AFFECTING THE CUSTOMS CLEARANCE TIME AT PRIME CUSTOMS OFFICE TYPE A OF TANJUNG PRIOK

2020 ◽  
Vol 2 (2) ◽  
pp. 01-17
Author(s):  
Khamami Herusantoso ◽  
Ardyanto Dwi Saputra
Keyword(s):  
Data Mining ◽  
Type A ◽  
Database Analysis ◽  
Data Set ◽  
Complex Phase ◽  
Specific Data ◽  
Factors Affecting ◽  
Efficiency And Effectiveness ◽  
Hidden Knowledge ◽  
Clearance Process

In the dwell-time, the customs clearance is considered as the most complex phase, even though its portion is the shortest among other phases, such as pre-clearance and post clearance. In order to improve the efficiency and effectiveness on the services performed in the customs clearance process, the customs authorities must start considering the help of database analysis in identifying obstacles instead of depending on the personal analysis. Useful information is hidden among the importation data set and it is extractable through data mining techniques. This study explores the customs clearance process of import cargo whose document is declared through the red channel at Prime Customs Office Type A of Tanjung Priok (PCO Tanjung Priok), and applies a specific data mining classifier called the decision tree with J48 algorithm to evaluate the process. There are 11 classification models developed using unpruned, online pruning, and post-pruning features. One best model is chosen to extract the hidden knowledge that describes factors affecting the customs clearance process and allows the customs authorities to improve their services performed in the future.

scholarly journals Research on Factors Affecting Real Estate Values by Data Mining

2018 ◽  
Vol 6 (1) ◽  
pp. 220-239 ◽  
Author(s):  
Filiz Ersoz ◽  
Taner Ersoz ◽  
Muhammet Soydan
Keyword(s):  
Data Mining ◽  
Real Estate ◽  
Statistical Modelling ◽  
Unit Price ◽  
City Centre ◽  
Important Place ◽  
Factors Affecting ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Valuation

Abstract Construction sector has an important place in Turkey’s economy. Real estate sales for the sector are increasing in parallel. However, the purchase cost is also important for those who are willing to buy a real estate. In the acquisition of real estate, factors such as size, location and age of the house are taken into consideration. The aim of the article is to conduct research on factors affecting real estate values by data mining. In this study, the most important variables that determine the value of the real estate have been investigated by data mining methods. The research has been carried out in Karabük and the variables determined according to the opinions of real estate experts. As classification methods, CHAID and C&RT algorithms have been used. It has been evaluated that both algorithm estimation results can be used. Within the framework of the study, the variables that have the most impact on the unit price have been determined, such as the size of the real estate, the distance to the city centre, the popularity, and the age of the building. The use of advanced technologies, such as statistical modelling and machine learning in real estate valuation and automatic value estimation, is of importance in determining the real value of the real estate.

Predicting the Academic Performance of Students Using Utility-Based Data Mining

2020 ◽  
pp. 56-85
Author(s):  
Sidath R. Liyanage ◽  
K. T. Sanvitha Kasthuriarachchi
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Tertiary Education ◽  
Analytical Framework ◽  
Machine Learning Algorithms ◽  
Educational Statistics ◽  
Factors Affecting ◽  
Educational Institute ◽  
Main Factors ◽  
Sphere Of Influence

Data mining in education has become an important topic in the sphere of influence of data mining. Mining educational data encompasses developing models, plotting data, and utilizing machine learning algorithms to derive patterns on educational data by attempting to uncover hidden patterns, create information for hidden relationships using educational statistics, and perform many more operations that are unfeasible using traditional computational tools. This research aims to identify the main factors that influence the academic performance of learners in tertiary education system in Sri Lanka. A conceptual framework and an analytical framework on factors affecting the academic performance was constructed with this aim. The analytical framework was then validated with the data collected from technology learners in a tertiary educational institute.

Quality Control of Micro-Drilled Hole Wall of PWBs Using Data-Mining

2005 ◽  
Author(s):  
Eiichi Aoyama ◽  
Toshiki Hirogaki ◽  
Keiji Ogawa ◽  
Tsuyoshi Otsuka ◽  
Katsutoshi Yamauchi
Keyword(s):  
Data Mining ◽  
Surface Roughness ◽  
Present Report ◽  
Weft Yarn ◽  
Hole Drilling ◽  
Printed Wiring Boards ◽  
Factors Affecting ◽  
Hole Wall ◽  
Using Data ◽  
Through Hole

In the manufacturing of printed wiring boards (PWBs), various methods have been developed in order to improve the circuit packaging density. Micro-drills are generally used to make smaller diameter through-holes in PWBs, which are desired for the miniaturization of equipment. However, a problem has emerged in that copper plating degraded by hole drilling can reduce the reliability of the electrical connection between layers. The surface roughness of drilled hole wall is one of the important factors affecting the plating quality. The purpose of the present report is to apply data-mining to the surface roughness data of drilled through-hole walls, and to elucidate the factors required to control the drilled hole quality. The following conclusions were obtained. (1) The data-mining aided by a computer was found to be effective to control the drilled hole wall quality in the PWBs manufacturing. (2) It was clear that the surface roughness of drilled hole walls depended on three factors: the drill temperature, cutting distance, and the width of the fiber bundle of weft yarn.

Sign in / Sign up

Export Citation Format

Share Document