scholarly journals The Simple Method to Assess Land Quality of Paddy Field Using Spectral, Soil pH and Statistical Regression Technique (Case Study of Paddy Field in Majalaya Subdistrict, Bandung Region)

2017 ◽  
Vol 2 (6) ◽  
pp. 194
Author(s):  
Mochamad Firman Ghazali ◽  
Agung Budi Harto ◽  
Ketut Wikantika
Keyword(s):  
Regression Model ◽  
Soil Ph ◽  
Paddy Field ◽  
Near Infrared ◽  
Landsat 8 ◽  
Statistical Regression ◽  
Simple Method ◽  
Land Quality

Assessing land quality has important use in understanding the capability of soil in producing food. The area of paddy fields in Majalaya Subdistrict is located around the industrial zone and this situation is urgent to understand the land quality of paddy field due to the influence effect of industrial waste to its growth. A combination of regression model and Landsat 8 image to estimate soil pH distribution is used to predict the land quality. The result of this study is shown that the regression model of red and near infrared (NIR) band combination is used to predict soil pH has been successfully given the smallest error (RMSe) as the soil pH accuracy is 1.18 and related to the land quality assessment based on predicted soil pH is shown that in the whole area of paddy field has the acid situation of soil pH.Keywords: Spectral, Soil pH; Regression, Land Quality; Land  Suitability

scholarly journals Prediction Modeling Studies for Medical Usage Rates in Mass Gatherings: A Systematic Review

2019 ◽  
Vol 34 (s1) ◽  
pp. s40-s40
Author(s):  
Hans Van Remoortel ◽  
Hans Scheers ◽  
Emmy De Buck ◽  
Karen Lauwers ◽  
Philippe Vandekerckhove
Keyword(s):  
Systematic Review ◽  
Regression Model ◽  
Free Water ◽  
Dew Point ◽  
Statistical Regression ◽  
Prediction Modeling ◽  
Mass Gatherings ◽  
Modeling Studies ◽  
Usage Rates

Introduction:Mass gatherings attended by large crowds are an increasingly common feature of society. In parallel, an increased number of studies have been conducted to identify those variables that are associated with increased medical usage rates.Aim:To identify studies that developed and/or validated a statistical regression model predicting patient presentation rate (PPR) or transfer to hospital rate (TTHR) at mass gatherings.Methods:Prediction modeling studies from 6 databases were retained following systematic searching. Predictors for PPR and/or TTHR that were included in a multivariate regression model were selected for analysis. The GRADE methodology (Grades of Recommendation, Assessment, Development, and Evaluation) was used to assess the quality of evidence.Results:We identified 11 prediction modeling studies with a combined audience of >32 million people in >1500 mass gatherings. Eight cross-sectional studies developed a prediction model in a mixed audience of (spectator) sports events, music concerts, and public exhibitions. Statistically significant variables (p<0.05) to predict PPR and/or TTHR were as follows: accommodation (seated, boundaries, indoor/outdoor, maximum capacity, venue access), type of event, weather conditions (humidity, dew point, heat index), crowd size, day vs night, demographic variables (age/gender), sports event distance, level of competition, free water availability, and specific TTHR-predictive factors (injury status: number of patient presentations, type of injury). The quality of the evidence was considered as low. Three studies externally validated their model against existing models. Two validation studies showed a large underestimation of the predicted patients presentations or transports to hospital (67-81%) whereas one study overestimated these outcomes by 10-28%.Discussion:This systematic review identified a comprehensive list of relevant predictors which should be measured to develop and validate future models to predict medical usage at mass gatherings. This will further scientifically underpin more effective pre-event planning and resource provision.

Human resources ranking in a cloud-based knowledge sharing framework using the quality control criteria

Kybernetes ◽  
2017 ◽  
Vol 46 (5) ◽  
pp. 876-892 ◽  
Author(s):  
Parisa Fouladi ◽  
Nima Jafari Navimipour
Keyword(s):  
Quality Control ◽  
Human Resources ◽  
Hierarchical Structure ◽  
Simple Method ◽  
Content Type ◽  
Quality Control Criteria ◽  
Control Criteria ◽  
Selection Of

Purpose This paper aims to propose a new method for evaluating the quality and prioritizing of the human resources (HRs) based on trust, reputation, agility, expertise and cost criteria in the expert cloud. To evaluate some quality control (QC) factors, a model based on the SERVQUAL is used. Design/methodology/approach The aim of this paper is to offer a fast and simple method for selecting the HRs by the customers. To achieve this goal, the ranking diagram of different HRs based on the different criteria of QC is provided. By means of this method, the customer can rapidly decide on the selection of the required HRs. By using the proposed method, the scores for various criteria are evaluated. These criteria are used in the ranking of each HR which is obtained based on the evaluation conducted by previous customers and their colleagues. First, customers were asked to select their needed criteria and then by constructing a hierarchical structure, the ranking diagram of different HRs is achieved. Using a ranking system based on evaluating the quality of the model, satisfy the customer needs to be based on the properties of HRs. Also, an analytical hierarchical process-based ranking mechanism is proposed to solve the problem of assigning weights to features for considering the interdependence between them to rank the HRs in the expert cloud. Findings The obtained results showed the applicability of the radar graph using a case study and also numerically obtained results showed that a hierarchical structure increases the quality and speed rating of HR ranking than the previous works. Originality/value The suggested ranking method in this paper allows the optimal selection due to the special needs of any given customer in the expert cloud.

scholarly journals The Effect of the Size of Traffic Analysis Zones on the Quality of Transport Demand Forecasts and Travel Assignments

2018 ◽  
Author(s):  
Mehmet Fatih Altan ◽  
Yunus Emre Ayözen
Keyword(s):  
Regression Model ◽  
Traffic Analysis ◽  
Model Parameters ◽  
The Public ◽  
Employment Density ◽  
Exponential Regression Model ◽  
Demand Forecasts

In this work we have studied the selection criteria for traffic analysis zones and the effects of their size and number on the model’s forecasting capabilities. To do so we have focused on the corridor of İstanbul’s Kadıköy-Kartal Metro Line and evaluated the consistency of demand forecasts and travel assignments versus actual measurements under different sizes of the Traffic Analysis Zones (TAZ). Significant improvements in model accuracy were observed by decreasing the zone size. Specifically, studying the public transport network assignments for the metro line when increasing the number of traffic analysis zones from 540 to 1,788 the root mean square error (RMSE) of forecasted vs. actual station-based counts was reduced by 23%. Subsequently, the study used population density and employment density as independent variables for the determination of the optimal radius for the 1,788 zone area, and applied an exponential regression model. Appropriate model parameters were derived for the above case study. The regression model resulted in R2 values over 0.62.

scholarly journals Development Of Landsat Cloud Free Image Data For Classification Of Land Cover-Case Study In Dak Lak Province

2019 ◽  
Vol 35 (4) ◽  
Author(s):  
Tran Anh Tuan ◽  
Nguyen Dinh Duong
Keyword(s):  
Land Cover ◽  
Optical Sensors ◽  
Image Data ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Earth Surface ◽  
Dry Seasons

Land cover mapping by optical remote sensing has many obstacles including clouds. Clouds block solar radiation coming to earth surface and reflective radiance from the earth surface to remote optical sensors resulting. Therefore, clouds result no-signal areas in images that cannot be used for study of ground objects. In many cases, thin clouds degrade quality of reflective radiance and some times alter, unexpectedly, spectral reflectance characteristics of ground objects leading to false classification. In this paper, the authors present an algorithm on application of multidate for development of cloud free image. The used image data were received in rainy and dry seasons and by stacking, cloud free images representing rainy and dry seasons were created. These cloud free images can be used further for classification of land cover in rainy and dry seasons. Experiments were conducted with Landsat 8 OLI images with path/row number 124/51 covering Dak Lak province of Vietnam. The results of case study were development of cloud free image data representing rainy and dry seasons allowing separation of evegreen and deciduous forests in the study site.  

Framework of decision in data modeling for quality improvement

2015 ◽  
Vol 27 (1) ◽  
pp. 135-149 ◽  
Author(s):  
Ângelo Márcio Oliveira Sant'Anna
Keyword(s):  
Experimental Data ◽  
Regression Model ◽  
Regression Models ◽  
Formal Analysis ◽  
Data Modeling ◽  
Industrial Processes ◽  
Statistical Regression ◽  
Mining Company ◽  
Content Type

Purpose – The purpose of this paper is to propose a framework of decision making to aid practitioners in modeling and optimization experimental data for improvement quality of industrial processes, reinforcing idea that planning and conducting data modeling are as important as formal analysis. Design/methodology/approach – The paper presents an application was carried out about the modeling of experimental data at mining company, with support at Catholic University from partnership projects. The literature seems to be more focussed on the data analysis than on providing a sequence of operational steps or decision support which would lead to the best regression model given for the problem that researcher is confronted with. The authors use the concept of statistical regression technique called generalized linear models. Findings – The authors analyze the relevant case study in mining company, based on best statistical regression models. Starting from this analysis, the results of the industrial case study illustrates the strong relationship of the improvement process with the presented framework approach into practice. Moreover, the case study consolidating a fundamental advantage of regression models: modeling guided provides more knowledge about products, processes and technologies, even in unsuccessful case studies. Research limitations/implications – The study advances in regression model for data modeling are applicable in several types of industrial processes and phenomena random. It is possible to find unsuccessful data modeling due to lack of knowledge of statistical technique. Originality/value – An essential point is that the study is based on the feedback from practitioners and industrial managers, which makes the analyses and conclusions from practical points of view, without relevant theoretical knowledge of relationship among the process variables. Regression model has its own characteristics related to response variable and factors, and misspecification of the regression model or their components can yield inappropriate inferences and erroneous experimental results.

scholarly journals Soil Quality as a Key Factor in Producing Vegetables for Home Consumption—A Case Study of Urban Allotments in Gorzów Wielkopolski (Poland)

Agronomy ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1836
Author(s):  
Maciej Bosiacki ◽  
Leszek Bednorz ◽  
Konstancja Fedeńczak ◽  
Tomasz Górecki ◽  
Andrzej Mizgajski ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Ph ◽  
C:N Ratio ◽  
Toxic Heavy Metals ◽  
Allotment Gardens ◽  
Key Factor ◽  
Calcium Magnesium ◽  
Cadmium And Lead

The aim of the study was to analyse the quality of soil in urban allotment gardens in the context of the production of home-grown vegetables. The study was conducted on six allotment gardens (31 individual plots) in Gorzów Wielkopolski, a medium-sized Polish city with an average level of industrialisation. The following soil characteristics were analysed: pH, electric conductivity, organic matter, organic carbon, humus, total nitrogen, C:N ratio, NH4+-N, NO3-N−, P, K, Ca, Mg, SO4−-S, Cl, Na, Fe, Cu, Zn, Mn, Ni, Cr, Cd, Pb. The analyses showed that the soils were abundant in necessary nutrients for vegetable growing. They had high content of calcium, magnesium, and phosphorus. However, the soil pH in areas of vegetable cropping was too high. The content of toxic heavy metals—cadmium (0.22–0.59 mg∙kg−1 d.m.) and lead (3.46–16.89 mg∙kg−1 d.m.)—was within the acceptable limits. Nevertheless, the chemical analysis of carrots used as test vegetables showed that the permissible limits of cadmium and lead content in their roots were exceeded. The excessive uptake of these toxic metals can be reduced by lowering the soil pH and applying organic carbon to the soil.

Analysis of Water Quality of Lake Hachiroko in Japan Using a Fuzzy Multiple Regression Model with ALOS AVNIR-2 Data

2016 ◽  
Vol 20 (6) ◽  
pp. 992-1003 ◽  
Author(s):  
Dejian Wang ◽  
◽  
Yoichi Kageyama ◽  
Makoto Nishida ◽  
Hikaru Shirai ◽  
...  
Keyword(s):  
Water Quality ◽  
Regression Model ◽  
Multiple Regression ◽  
Near Infrared ◽  
Multiple Regression Model ◽  
Fuzzy Regression ◽  
Quality Data ◽  
Water Quality Data ◽  
Using Data

The distribution of water pollution is often assessed by remote sensing. In this study, we develop a fuzzy multiple regression model and analyze water quality using data collected by the Advanced Visible and Near Infrared Radiometer type-2 (AVNIR-2) of the Advanced Land Observing Satellite at different time points. We conduct a fuzzy multiple regression analysis of the AVNIR-2 data and direct measurements of the local water quality of Lake Hachiroko in Japan. The relationship between the AVNIR-2 and water quality data are analyzed by solving both min and max problems. We compare the estimated water quality maps with the actual distributions in the study area, and determine that the proposed method enables us to derive water quality conditions effectively from the AVNIR-2 data. Furthermore, by comparing maps created using AVNIR-2 data collected at different times, we obtain results revealing temporal changes in water quality. In addition, we compare maps created using the fuzzy multiple regression and fuzzy regression models. We demonstrate that the former offers a greater number of solutions and provides more details about water quality.

scholarly journals An Empirical Radiometric Intercomparison Methodology Based on Global Simultaneous Nadir Overpasses Applied to Landsat 8 and Sentinel-2

2020 ◽  
Vol 12 (17) ◽  
pp. 2736
Author(s):  
Jorge Gil ◽  
Juan Fernando Rodrigo ◽  
Pablo Salvador ◽  
Diego Gómez ◽  
Julia Sanz ◽  
...  
Keyword(s):  
Optical Sensors ◽  
Near Infrared ◽  
Atmospheric Effects ◽  
Landsat 8 ◽  
Climate Data ◽  
Wide Range ◽  
Highly Correlated ◽  
Sentinel 2A ◽  
The Relationship

The Simultaneous Nadir Overpass (SNO) method was developed by the NOAA/NESDIS to improve the consistency and quality of climate data acquired by different meteorological satellites. Taking advantage of the reduced impact induced by the Bidirectional Reflectance Distribution Function (BRDF), atmospheric effects, illumination and viewing geometries during an SNO, we created a sensor comparison methodology for all spectral targets. The method is illustrated by applying it to the assessment of data acquired by the Landsat 8 (L8), Sentinel-2A (S2A), and Sentinel-2B (S2B) optical sensors. Multiple SNOs were identified and selected without the need for orbit propagators. Then, by locating spatially homogeneous areas, it was possible to assess, for a wide range of Top-of-Atmosphere reflectance values, the relationship between the L8 bands and the corresponding ones of S2A and S2B. The results yield high coefficients of determination for S2 A/B with respect to L8. All are higher than 0.980 for S2A and 0.984 for S2B. If the S2 band 8 (wide near-infrared, NIR) is excluded then the lowest coefficients of determination become 0.997 and 0.999 from S2A and S2B, respectively. This methodology can be complementary to those based on Pseudo-Invariant Calibration Sites (PICS) due to its simplicity, highly correlated results and the wide range of compared reflectances and spectral targets.

scholarly journals The Spatial Distribution Characteristics of the Cultivated Land Quality in the Diluvial Fan Terrain of the Arid Region: A Case Study of Jimsar County, Xinjiang, China

Land ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 896
Author(s):  
Yang Sheng ◽  
Weizhong Liu ◽  
Hailiang Xu ◽  
Xianchao Gao
Keyword(s):  
Ordinary Kriging ◽  
Arid Region ◽  
Spatial Interpolation ◽  
Quality Evaluation ◽  
Comprehensive Evaluation ◽  
Land Evaluation ◽  
Cultivated Land ◽  
Land Quality

Environmental constraints are not only important aspects that affect the cultivated land quality but also necessary factors that shall be considered when evaluating the cultivated land quality scientifically. Moreover, identifying the quality condition of cultivated land accurately is the premise for guaranteeing food security. Based on the case study of diluvial fan terrain in Jimsar County, Xinjiang in the arid region of Northwest China, this study utilizes a geographic information system spatial analysis and a multifactor comprehensive evaluation method and constructs a comprehensive evaluation index system for cultivated land quality on account of three dimensions, namely soil properties, farming conditions, and natural environmental conditions. To reduce the Modifiable Areal Unit Problem (MAUP) effect and improve the accuracy of the quality evaluation results of cultivated land, this study compares the spatial interpolation methods of Inverse Distance Weighted Matrix (IDW), Ordinary Kriging (OK), and Spline Functions (Spline) based on different cultivated land evaluation units. Through the assessment on the comparison results, we finally adopted large-scale cultivated land as the quality evaluation unit of cultivated land and Ordinary Kriging (OK) as the spatial interpolation method. The results indicated that the average grade of the quality index of cultivated land in the diluvial fan terrain of Jimsar County is 6.66 at the middle or lower level; the quality of cultivated land and natural environment conditions reduce with the rise of elevation of the diluvial fan terrain, indicating a vertical zonality differentiation rule; the farming conditions keep sliding from the middle part of diluvial fan terrain to the edge of the diluvial fan terrain and the piedmont slope. The major factors affecting the quality of the cultivated land include the soil capacity, soil pH, soil organic matter, the quantity of straw returning to the field, source of irrigation water, water delivery method, part of the diluvial fan, groundwater level depth, and geomorphic type. Therefore, the measures to improve the quality of the cultivated land are put forward, mainly including improving the soil, carrying out land consolidation projects, and developing highly efficient water-saving irrigation agriculture. This study provides favorable references and directions for the sustainable utilization and quality improvement of cultivated land resources in arid regions.

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