scholarly journals A TOOL FOR MACHINE LEARNING BASED DASYMETRIC MAPPING APPROACHES IN GRASS GIS

2021 ◽  
Vol XLVI-4/W2-2021 ◽  
pp. 55-62
Author(s):  
C. Flasse ◽  
T. Grippa ◽  
S. Fennia
Keyword(s):  
Machine Learning ◽  
Spatial Resolution ◽  
Demographic Data ◽  
Dasymetric Mapping ◽  
Grass Gis ◽  
Levels Of Detail ◽  
Scale Layer ◽  
Open Source Gis ◽  
Administrative Units ◽  
Gis Software

Abstract. Socio-economic and demographic data is often released at the level of census administrative units. However, there is often a need for data available at a higher spatial resolution. Dasymetric mapping is an approach that can be used to disaggregate such data into finer levels of detail. It relies on the assumption that proxies available at a higher spatial resolution, along with knowledge of an area, can be used to produce weights in order to spatially reallocate the data to a finer scale layer. The power and efficiency of machine learning (ML) approaches can be taken advantage of when producing weighted layers for dasymetric mapping. Less advanced users, however, may find these approaches too complex. To encourage a wider uptake of such approaches, easy-to-use tools are necessary. GRASS GIS is a free and open-source GIS software that contains many modules for processing geographic data. The existing GRASS GIS add-on “v.area.weigh” already makes the dasymetric mapping approach more accessible, however users must provide their own weighted layer. This paper presents the development of a GRASS GIS add-on, “r.area.createweight”, which provides a simple and convenient tool to facilitate the implementation of a ML-based approach to produce weighted layers for dasymetric mapping. The tool will be available on the official GRASS GIS add-on repository to encourage a more widespread uptake of these approaches.

scholarly journals Semi-automated classification of colonial Microcystis by FlowCAM imaging flow cytometry in mesocosm experiment reveals high heterogeneity during seasonal bloom

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yersultan Mirasbekov ◽  
Adina Zhumakhanova ◽  
Almira Zhantuyakova ◽  
Kuanysh Sarkytbayev ◽  
Dmitry V. Malashenkov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
Spatial Resolution ◽  
Mesocosm Experiment ◽  
Imaging Flow Cytometry ◽  
Leibler Divergence ◽  
Temporal And Spatial ◽  
High Level ◽  
Training Sets

AbstractA machine learning approach was employed to detect and quantify Microcystis colonial morphospecies using FlowCAM-based imaging flow cytometry. The system was trained and tested using samples from a long-term mesocosm experiment (LMWE, Central Jutland, Denmark). The statistical validation of the classification approaches was performed using Hellinger distances, Bray–Curtis dissimilarity, and Kullback–Leibler divergence. The semi-automatic classification based on well-balanced training sets from Microcystis seasonal bloom provided a high level of intergeneric accuracy (96–100%) but relatively low intrageneric accuracy (67–78%). Our results provide a proof-of-concept of how machine learning approaches can be applied to analyze the colonial microalgae. This approach allowed to evaluate Microcystis seasonal bloom in individual mesocosms with high level of temporal and spatial resolution. The observation that some Microcystis morphotypes completely disappeared and re-appeared along the mesocosm experiment timeline supports the hypothesis of the main transition pathways of colonial Microcystis morphoforms. We demonstrated that significant changes in the training sets with colonial images required for accurate classification of Microcystis spp. from time points differed by only two weeks due to Microcystis high phenotypic heterogeneity during the bloom. We conclude that automatic methods not only allow a performance level of human taxonomist, and thus be a valuable time-saving tool in the routine-like identification of colonial phytoplankton taxa, but also can be applied to increase temporal and spatial resolution of the study.

scholarly journals Predictive modeling for peri-implantitis by using machine learning techniques

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tomoaki Mameno ◽  
Masahiro Wada ◽  
Kazunori Nozaki ◽  
Toshihito Takahashi ◽  
Yoshitaka Tsujioka ◽  
...  
Keyword(s):  
Machine Learning ◽  
Demographic Data ◽  
Risk Indicators ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Machine Learning Methods ◽  
Complex Interactions ◽  
Learning Techniques ◽  
Increased Risk ◽  
Vector Machines

AbstractThe purpose of this retrospective cohort study was to create a model for predicting the onset of peri-implantitis by using machine learning methods and to clarify interactions between risk indicators. This study evaluated 254 implants, 127 with and 127 without peri-implantitis, from among 1408 implants with at least 4 years in function. Demographic data and parameters known to be risk factors for the development of peri-implantitis were analyzed with three models: logistic regression, support vector machines, and random forests (RF). As the results, RF had the highest performance in predicting the onset of peri-implantitis (AUC: 0.71, accuracy: 0.70, precision: 0.72, recall: 0.66, and f1-score: 0.69). The factor that had the most influence on prediction was implant functional time, followed by oral hygiene. In addition, PCR of more than 50% to 60%, smoking more than 3 cigarettes/day, KMW less than 2 mm, and the presence of less than two occlusal supports tended to be associated with an increased risk of peri-implantitis. Moreover, these risk indicators were not independent and had complex effects on each other. The results of this study suggest that peri-implantitis onset was predicted in 70% of cases, by RF which allows consideration of nonlinear relational data with complex interactions.

scholarly journals A Comparison of Precise Fertilization Prescription Rates to a Conventional Approach Based on the Open Source GIS Software

Poljoprivreda ◽  
2021 ◽  
Vol 27 (1) ◽  
pp. 52-59
Author(s):  
Mladen Jurišić ◽  
◽  
Dorijan Radočaj ◽  
Ivan Plaščak ◽  
Irena Rapčan
Keyword(s):  
Open Source ◽  
Precision Agriculture ◽  
Spatial Interpolation ◽  
Crop Yields ◽  
Conventional Approach ◽  
Optimal Interpolation ◽  
Phosphorous Pentoxide ◽  
Open Source Gis ◽  
Prescription Rates ◽  
Gis Software

Fertilization is one of the most important components of precision agriculture, ensuring high and stable crop yields. The process of spatial interpolation of soil sample data is recognized as a reliable method of determining the prescription rates for precise fertilization. However, the application of a free open-source geographic information system (GIS) software was often overlooked in the process. In this study, a method of precise fertilization prescription map creation was developed using an open-source GIS software to enable a wider and cheaper availability of its application. The study area covered three independent locations in Osijek-Baranja County. A method was developed for the fertilization of sugar beet with phosphorous pentoxide, but its application is universal with regard to the crop type. An ordinary kriging was determined as an optimal interpolation method for spatial interpolation, with the mean RMSE of 1.8754 and R2of 0.6955. By comparing the precision fertilization prescription rates to a conventional approach, the differences of 4.1 kg ha-1 for Location 1, 15.8 kg ha-1 for Location 2, and 11.2 kg ha-1 for Location 3 were observed. These values indicate a general deficit in soil phosphorous pentoxide, and precise fertilization could ensure its optimal content in the future sowing seasons.

scholarly journals Customer Churn in Retail E-Commerce Business: Spatial and Machine Learning Approach

2022 ◽  
Vol 17 (1) ◽  
pp. 165-198
Author(s):  
Kamil Matuszelański ◽  
Katarzyna Kopczewska
Keyword(s):  
Machine Learning ◽  
Urban Areas ◽  
Latent Dirichlet Allocation ◽  
Demographic Data ◽  
Numerical Data ◽  
Gradient Boosting ◽  
Modern Approach ◽  
Rural And Urban Areas ◽  
Customer Churn ◽  
Extreme Gradient Boosting

This study is a comprehensive and modern approach to predict customer churn in the example of an e-commerce retail store operating in Brazil. Our approach consists of three stages in which we combine and use three different datasets: numerical data on orders, textual after-purchase reviews and socio-geo-demographic data from the census. At the pre-processing stage, we find topics from text reviews using Latent Dirichlet Allocation, Dirichlet Multinomial Mixture and Gibbs sampling. In the spatial analysis, we apply DBSCAN to get rural/urban locations and analyse neighbourhoods of customers located with zip codes. At the modelling stage, we apply machine learning extreme gradient boosting and logistic regression. The quality of models is verified with area-under-curve and lift metrics. Explainable artificial intelligence represented with a permutation-based variable importance and a partial dependence profile help to discover the determinants of churn. We show that customers’ propensity to churn depends on: (i) payment value for the first order, number of items bought and shipping cost; (ii) categories of the products bought; (iii) demographic environment of the customer; and (iv) customer location. At the same time, customers’ propensity to churn is not influenced by: (i) population density in the customer’s area and division into rural and urban areas; (ii) quantitative review of the first purchase; and (iii) qualitative review summarised as a topic.

scholarly journals ELECTRICITY INFRASTRUCTURE PLANNING: EVALUATING SOLAR POTENTIAL ASSESSMENTS FOR INFORMAL SETTLEMENTS USING GRASS AND FREELY AVAILABLE DATA

2018 ◽  
Vol XLII-4/W8 ◽  
pp. 69-76
Author(s):  
L. Hassim ◽  
S. Coetzee ◽  
V. Rautenbach
Keyword(s):  
Informal Settlement ◽  
Informal Settlements ◽  
Urban Fringe ◽  
Safe Alternative ◽  
Open Source Gis ◽  
Basic Services ◽  
Alternative Sources ◽  
Gis Software ◽  
The City ◽  
Electricity Infrastructure

<p><strong>Abstract.</strong> Informal settlements, also known as slums or shanty towns, are characterised by rapid and unstructured expansion, poorly constructed buildings, and in some cases, they are on disputed land. Such settlements often lack basic services, such as electricity. As a result, informal settlement dwellers turn to hazardous alternative sources of energy, such as illegal electricity connections and paraffin. Solar power is a clean and safe alternative. However, informal settlements are often located on undesirable land on the urban fringe where the topography may hinder the use of solar energy. The high density of dwellings could also be a hindrance. Therefore, the solar potential needs to be assessed before any implementations are planned. Solar potential assessment functionality is generally available in geographic information system (GIS) products. The nature, cost and accessibility of datasets required for the assessment vary significantly. In this paper, we evaluate the results of solar potential assessments using GRASS (Geographic Resources Analysis Support System) for a number of different datasets. The assessments were done for two informal settlements in the City of Tshwane (South Africa): Alaska, which is nestled on a hill; and Phomolong, a densely populated settlement with a rather flat topography. The results show that solar potential assessments with open source GIS software and freely available data are feasible. This eliminates the need for lengthy and bureaucratic procurement processes and reduces the financial costs of assessing solar potential for informal settlements.</p>

scholarly journals Machine Learning Algorithm Using Clinical Data and Demographic Data for Preterm Birth Prediction

2022 ◽  
Vol 226 (1) ◽  
pp. S362-S363
Author(s):  
Matthew Hoffman ◽  
Wei Liu ◽  
Jade Tunguhan ◽  
Ghamar Bitar ◽  
Kaveeta Kumar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Preterm Birth ◽  
Clinical Data ◽  
Learning Algorithm ◽  
Demographic Data ◽  
Machine Learning Algorithm
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