scholarly journals MODELING URBAN DYNAMICS USING RANDOM FOREST: IMPLEMENTING ROC AND TOC FOR MODEL EVALUATION

2016 ◽  
Vol XLI-B2 ◽  
pp. 285-290 ◽  
Author(s):  
M. Ahmadlou ◽  
M. R. Delavar ◽  
H. Shafizadeh-Moghadam ◽  
A. Tayyebi
Keyword(s):  
Land Use ◽  
Random Forest ◽  
Urban Growth ◽  
High Performance ◽  
Operating Characteristics ◽  
Urban Dynamics ◽  
Suitability Map ◽  
And Performance ◽  
Complex Relationships ◽  
Landsat Satellite

The importance of spatial accuracy of land use/cover change maps necessitates the use of high performance models. To reach this goal, calibrating machine learning (ML) approaches to model land use/cover conversions have received increasing interest among the scholars. This originates from the strength of these techniques as they powerfully account for the complex relationships underlying urban dynamics. Compared to other ML techniques, random forest has rarely been used for modeling urban growth. This paper, drawing on information from the multi-temporal Landsat satellite images of 1985, 2000 and 2015, calibrates a random forest regression (RFR) model to quantify the variable importance and simulation of urban change spatial patterns. The results and performance of RFR model were evaluated using two complementary tools, relative operating characteristics (ROC) and total operating characteristics (TOC), by overlaying the map of observed change and the modeled suitability map for land use change (error map). The suitability map produced by RFR model showed 82.48% area under curve for the ROC model which indicates a very good performance and highlights its appropriateness for simulating urban growth.

scholarly journals MODELING URBAN DYNAMICS USING RANDOM FOREST: IMPLEMENTING ROC AND TOC FOR MODEL EVALUATION

2016 ◽  
Vol XLI-B2 ◽  
pp. 285-290 ◽  
Author(s):  
M. Ahmadlou ◽  
M. R. Delavar ◽  
H. Shafizadeh-Moghadam ◽  
A. Tayyebi
Keyword(s):  
Land Use ◽  
Random Forest ◽  
Urban Growth ◽  
High Performance ◽  
Operating Characteristics ◽  
Urban Dynamics ◽  
Suitability Map ◽  
And Performance ◽  
Complex Relationships ◽  
Landsat Satellite

The importance of spatial accuracy of land use/cover change maps necessitates the use of high performance models. To reach this goal, calibrating machine learning (ML) approaches to model land use/cover conversions have received increasing interest among the scholars. This originates from the strength of these techniques as they powerfully account for the complex relationships underlying urban dynamics. Compared to other ML techniques, random forest has rarely been used for modeling urban growth. This paper, drawing on information from the multi-temporal Landsat satellite images of 1985, 2000 and 2015, calibrates a random forest regression (RFR) model to quantify the variable importance and simulation of urban change spatial patterns. The results and performance of RFR model were evaluated using two complementary tools, relative operating characteristics (ROC) and total operating characteristics (TOC), by overlaying the map of observed change and the modeled suitability map for land use change (error map). The suitability map produced by RFR model showed 82.48% area under curve for the ROC model which indicates a very good performance and highlights its appropriateness for simulating urban growth.

scholarly journals Optimization of random forest model for assessing and predicting geological hazards susceptibility in Lingyun County

2021 ◽  
Author(s):  
Chunfang Kong ◽  
Junzuo Wang ◽  
Xiaogang Ma ◽  
Yiping Tian ◽  
Zhiting Zhang ◽  
...  
Keyword(s):  
Land Use ◽  
Random Forest ◽  
Vegetation Index ◽  
Evaluation Model ◽  
Field Investigation ◽  
Slope Aspect ◽  
Geological Hazards ◽  
Operating Characteristics ◽  
Model Based ◽  
Prediction And Prevention

Abstract The frequent occurrence of geological hazards will not only cause peoples' property loss and deterioration of living environments, but will also endanger peoples' lives. Therefore, rapid and accurate evaluation of geological hazards susceptibility can provide an important scientific basis for emergency rescue and disaster reduction and prevention. In this paper, ten effective variables including slope, aspect, curvature, normalized differential vegetation index, annual precipitation, strata lithology, tectonic complexity, residential density, road network density, and land use/land cover were selected as evaluation indexes. Meanwhile, random forest (RF) model is improved by the optimization of unbalanced geological hazards dataset, differentiation of continuous geological hazards evaluation factors, sample similarity calculation, and iterative method for finding optimal random characteristics by calculating out-of-bagger errors. The geological hazards susceptibility evaluation model based on optimized RF (OPRF) was established and used to assess the susceptibility level of geological hazards for Lingyun County. Then, receiver operating characteristics (ROC) curves and field investigation were performed to verify the efficiency for five models. Analysis and comparison of the results denoted that the model based on OPRF has the highest prediction accuracy of 93.4%, which is far better than the other four models. Furthermore, the evaluation results can provide reference for geological hazards prediction and prevention, and can also provide decision support for land use development and rational utilization of resources and environment in Lingyun County. Based on these results, the OPRF model could be extended to other regions with similar geological environment backgrounds for geological hazards susceptibility assessment and prediction.

scholarly journals Prediction of Antimalarial Drug-Decorated Nanoparticle Delivery Systems with Random Forest Models

Biology ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 198
Author(s):  
Diana V. Urista ◽  
Diego B. Carrué ◽  
Iago Otero ◽  
Sonia Arrasate ◽  
Viviana F. Quevedo-Tumailli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Information Fusion ◽  
High Performance ◽  
Antimalarial Activity ◽  
Classification Model ◽  
Operating Characteristics ◽  
Experimental Conditions ◽  
Nanoparticle Delivery ◽  
Random Forest Models

Drug-decorated nanoparticles (DDNPs) have important medical applications. The current work combined Perturbation Theory with Machine Learning and Information Fusion (PTMLIF). Thus, PTMLIF models were proposed to predict the probability of nanoparticle–compound/drug complexes having antimalarial activity (against Plasmodium). The aim is to save experimental resources and time by using a virtual screening for DDNPs. The raw data was obtained by the fusion of experimental data for nanoparticles with compound chemical assays from the ChEMBL database. The inputs for the eight Machine Learning classifiers were transformed features of drugs/compounds and nanoparticles as perturbations of molecular descriptors in specific experimental conditions (experiment-centered features). The resulting dataset contains 107 input features and 249,992 examples. The best classification model was provided by Random Forest, with 27 selected features of drugs/compounds and nanoparticles in all experimental conditions considered. The high performance of the model was demonstrated by the mean Area Under the Receiver Operating Characteristics (AUC) in a test subset with a value of 0.9921 ± 0.000244 (10-fold cross-validation). The results demonstrated the power of information fusion of the experimental-centered features of drugs/compounds and nanoparticles for the prediction of nanoparticle–compound antimalarial activity. The scripts and dataset for this project are available in the open GitHub repository.

scholarly journals Urban Growth Assessment of Chennai District by Remote Sensing and Gis Techniques

2020 ◽  
Vol 8 (6) ◽  
pp. 5119-5125
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Urban Growth ◽  
Satellite Images ◽  
Remote Sensing And Gis ◽  
Land Use Land Cover ◽  
Gis Techniques ◽  
Land Cover Map ◽  
Landsat Satellite

Urban growth of Chennai district is exponential and heading towards extreme urbanisation. Hence this necessitates the study of urban growth in Chennai district. The recent advancement in Remote sensing and GIS has an excellent ability to derive various data from the satellite images obtained .This helps us to map, monitor and picturise various aspects of development with respect to their demands. The basic principle of remote sensing is followed as the methodology. By following the methodology correctly and by proper processing of the data acquired from the satellite images, the exact requirements of information can be obtained. The Change in the urban growth of the Chennai district for three decades from 1989 to 2019 have been found by using remote sensing and GIS techniques. The satellite images of various years are obtained from Landsat satellite from the USGS Earth Explorer .The Land use characteristics of Chennai district of each year can be obtained by preparing the land use land cover map of Chennai district by the use of landsat satellite images. The two software namely ArcGIS and ERDAS Imagine are used to create the Land use land cover map. From the Land use land cover map of Chennai district, the change detection and statistical analysis of three decades are done and these analysis clearly shows that the urban growth of Chennai district is constantly increasing and there is a huge decrease in other natural features such as vegetation, water body and barren land. By performing urban trend analysis the urban growth of Chennai district for the upcoming years are predicted to prove the urban agglomeration in Chennai district.

scholarly journals Spatial Monitoring of Urban Expansion Using Satellite Remote Sensing Images: A Case Study of Amman City, Jordan

2019 ◽  
Vol 11 (8) ◽  
pp. 2260 ◽  
Author(s):  
Hussam Al-Bilbisi
Keyword(s):  
Land Use ◽  
Urban Area ◽  
Urban Growth ◽  
Urban Areas ◽  
Urban Expansion ◽  
Capital City ◽  
Annual Rate ◽  
Average Annual Rate ◽  
Rate Of Increase ◽  
Landsat Satellite

Amman, the capital city of Jordan, faces urbanization challenges and lacks reliable data for urban planning. This study is aimed at assessing, monitoring, and mapping urban land cover using multitemporal Landsat satellite images. Four different land use/cover maps were produced; periods of over ten years between 1987 and 2017 (i.e., in 1987, 1997, 2007, and 2017) were used to evaluate and analyze urban expansion visually and quantitatively. Supervised classification technique followed by the post classification comparison change detection approach was used to analyze images. Over the past three decades, the urban area has increased rapidly in Amman. It increased by 90.78 km2, from 149.08 km2 in 1987 to 237.86 km2 in 2017, with an average annual rate of increase of 2.03%. Urban area increases were significantly higher in the first 10 years of the study period (i.e., from 1987 to 1997), during which the average annual rate of increase reached 3.33%, while it was 2.04% for the last two decades of the study period (i.e., from 1997 to 2017). Urban growth in Amman generally occurred along transport routes away from the core of Amman, and as a result, this growth led to the expansion of urban areas into other types of land use/cover classes, particularly vegetation areas. The spatial analysis of urban expansion and trends of urban growth in Amman could provide the required input data for the urban modeling of the city.

scholarly journals New Urban Economic Agents: A Comparative Analysis of High-Performance New Entrepreneurs

2016 ◽  
Vol 35 (4) ◽  
pp. 5-22 ◽  
Author(s):  
Karima Kourtit ◽  
Peter Nijkamp ◽  
Soushi Suzuki
Keyword(s):  
Business Performance ◽  
High Performance ◽  
Business Strategies ◽  
Economic Implications ◽  
Economic Agents ◽  
Main Research ◽  
Large Cities ◽  
Urban Dynamics ◽  
Migrant Entrepreneurs ◽  
And Performance

Abstract Migrants are often the carriers of new skills and original abilities. This study focuses on the importance of ‘new urban entrepreneurship’ - in particular, ethnic or migrant business firms - as a major driver of creative and urban dynamics and economic vitality in urban agglomerations. The paper offers a general account of both backgrounds and socio-economic implications of migrant entrepreneurship in large agglomerations and highlights the socio-economic heterogeneity in motivation and performance among different groups of migrant entrepreneurs. This demographic- cultural diversity prompts intriguing questions about differences in business performance among distinct groups of migrant entrepreneurs, even in the same ethnic group. In the paper, a recently developed and amended version of data envelopment analysis (DEA), viz. super-efficiency, is presented and applied to a group of Moroccan entrepreneurs in four large cities in the Netherlands. The main research aim is (i) to identify the best-performing firms (so-called ‘entrepreneurial heroes’) from a broad management and business perspective, while (ii) the background of our findings are more thoroughly analysed. The paper ends with some general concluding remarks on urban business strategies.

Trace nanoanalysis

1994 ◽  
Vol 52 ◽  
pp. 940-941
Author(s):  
D. E. Newbury ◽  
R. D. Leapman
Keyword(s):  
High Performance ◽  
Secondary Ion Mass Spectrometry ◽  
Detection Efficiency ◽  
Volume Element ◽  
And Performance ◽  
Trace Constituents ◽  
Secondary Ion ◽  
Concentration Levels ◽  
Structure Properties

Trace constituents, which can be very loosely defined as those present at concentration levels below 1 percent, often exert influence on structure, properties, and performance far greater than what might be estimated from their proportion alone. Defining the role of trace constituents in the microstructure, or indeed even determining their location, makes great demands on the available array of microanalytical tools. These demands become increasingly more challenging as the dimensions of the volume element to be probed become smaller. For example, a cubic volume element of silicon with an edge dimension of 1 micrometer contains approximately 5×1010 atoms. High performance secondary ion mass spectrometry (SIMS) can be used to measure trace constituents to levels of hundreds of parts per billion from such a volume element (e. g., detection of at least 100 atoms to give 10% reproducibility with an overall detection efficiency of 1%, considering ionization, transmission, and counting).

https://imsciences.edu.pk/files/journals/vol12_2/New%201%20MA.864.pdf

2020 ◽  
Vol 12 (2) ◽  
pp. 19-50 ◽  
Author(s):  
Muhammad Siddique ◽  
Shandana Shoaib ◽  
Zahoor Jan
Keyword(s):  
Organizational Performance ◽  
Structural Equation ◽  
High Performance ◽  
Service Sector ◽  
Performance Outcomes ◽  
Theory And Practice ◽  
Relational Coordination ◽  
Multiple Sources ◽  
And Performance ◽  
High Level

A key aspect of work processes in service sector firms is the interconnection between tasks and performance. Relational coordination can play an important role in addressing the issues of coordinating organizational activities due to high level of interdependence complexity in service sector firms. Research has primarily supported the aspect that well devised high performance work systems (HPWS) can intensify organizational performance. There is a growing debate, however, with regard to understanding the “mechanism” linking HPWS and performance outcomes. Using relational coordination theory, this study examines a model that examine the effects of subsets of HPWS, such as motivation, skills and opportunity enhancing HR practices on relational coordination among employees working in reciprocal interdependent job settings. Data were gathered from multiple sources including managers and employees at individual, functional and unit levels to know their understanding in relation to HPWS and relational coordination (RC) in 218 bank branches in Pakistan. Data analysis via structural equation modelling, results suggest that HPWS predicted RC among officers at the unit level. The findings of the study have contributions to both, theory and practice.

scholarly journals Study on strong spinning preparation method and mechanism of the industrial pure titanium ultrafine crystallization

2019 ◽  
Vol 14 ◽  
pp. 155892501989525
Author(s):  
Yu Yang ◽  
Yanyan Jia
Keyword(s):  
Heat Treatment ◽  
High Performance ◽  
Pure Titanium ◽  
Grain Structure ◽  
Theoretical Research ◽  
Ultrafine Grain ◽  
Forming Process ◽  
Spinning Process ◽  
And Performance ◽  
Material Utilization

Ultrafine crystallization of industrial pure titanium allowed for higher tensile strength, corrosion resistance, and thermal stability and is therefore widely used in medical instrumentation, aerospace, and passenger vehicle manufacturing. However, the ultrafine crystallizing batch preparation of tubular industrial pure titanium is limited by the development of the spinning process and has remained at the theoretical research stage. In this article, the tubular TA2 industrial pure titanium was taken as the research object, and the ultrafine crystal forming process based on “5-pass strong spin-heat treatment-3 pass-spreading-heat treatment” was proposed. Based on the spinning process test, the ultimate thinning rate of the method is explored and the evolution of the surface microstructure was analyzed by metallographic microscope. The research suggests that the multi-pass, medium–small, and thinning amount of spinning causes the grain structure to be elongated in the axial and tangential directions, and then refined, and the axial fiber uniformity is improved. The research results have certain scientific significance for reducing the consumption of high-performance metals improving material utilization and performance, which also promote the development of ultrafine-grain metals’ preparation technology.

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