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Area and Feature Guided Regularised Random Forest: a novel method for predictive modelling of binary phenomena. The case of illegal landfill in Canary Island

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INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 2022年第12期36卷 2473-2495页

作者： Quesada-Ruiz, Lorenzo Carlos Rodriguez-Galiano, Victor Francisco Zurita-Milla, Raul Izquierdo-Verdiguier, Emma Univ Seville Dept Phys Geog & Reg Geog Anal Seville Spain Univ Twente Fac Geoinformat Sci & Earth Observat ITC Enschede Netherlands Univ Nat Resources & Life Sci Inst Geomat Vienna BOKU Vienna Austria

This paper presents a novel method, Area and Feature Guided Regularised Random Forest (AFGRRF), applied for modelling binary geographic phenomenon (occurrence versus absence). AFGRRF is a wrapper feature-selection method based on a previous modification of Random Forest (RF), namely the Guided Regularised Random Forest (GRRF). AFGRRF produces maps that minimise the affected area without a significant difference in accuracy. For this, it tunes the GRRF hyper-parameters according to a trade of between True Positive Rate and the affected area (Success Rate). AFGRRF also addresses the 'Rashomon effect' or the multiplicity of good models. The proposed method was tested to model illegal landfills in Gran Canaria Island (Spain). AFGRRF performance was compared to that of other RF-based methods: (i) standard RF;(ii) Area Random Forest (ARF);(iii) Feature Random Forest (FRF);(iv) Area Feature Random Forest (AFRF) and (v) GRRF. AFGRRF predicted the smallest affected area, 19% of the island, at a similar True Positive Rate. This percentage is substantially smaller than the one predicted by RF (27.43%), ARF (26%), FRF (27.78%), AFRF (23%) and GRRF (29.67%).

关键词： Random Forest feature selection predictive modelling binary phenomena success rate illegal landfill

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SVM-Based predictive modelling of Wet Pelletization Using Experimental and GA-Based Synthetic Data

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2016年第3期41卷 1053-1065页

作者： Nadeem, Mohammad Banka, Haider Venugopal, R. Indian Sch Mines Dept Comp Sci & Engn Dhanbad 826004 Bihar India Indian Sch Mines Dept Fuel & Mineral Engn Dhanbad 826004 Bihar India

A soft computing-based approach to capture the influence of process parameters on wet pelletization is presented. Wet pelletization is highly susceptible to even the minute change in operating conditions that results in substantial changes in product characteristics. This sensitive nature inspired research towards the development of new models for forecasting pellet attributes and increasing process understanding. The present modelling approach is based on support vector machine (SVM), a soft computing technique, to predict wet pellet characteristics and to analyse the effect of individual variable parameters. Experiments are carried out on a laboratory scale disc pelletizer with changing parameter values. However, experimental data were insufficient to train SVM and afterwards to test its prediction accuracy. To accomplish this task, genetic algorithm is employed to generate synthetic data and its fitness functions are derived using multiple regression. SVM is trained and validated on synthetic data while original data are used for testing. The significance of process parameters on pellet characteristics is also examined by deliberately removing them one by one from the model and recording the corresponding variation in prediction accuracy. The study employing SVM for the first time establishes the predictive ability of SVM with precision in wet pelletization.

关键词： Granulation Wet pelletization predictive modelling Soft computing Support vector machine

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Species richness coincidence: conservation strategies based on predictive modelling

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BIODIVERSITY AND CONSERVATION 2005年第6期14卷 1345-1364页

作者： Maes, D Bauwens, D De Bruyn, L Anselin, A Vermeersch, G Van Landuyt, W De Knijf, G Gilbert, M Inst Nat Conservat B-1070 Brussels Belgium Univ Antwerp Dept Biol B-2020 Antwerp Belgium Univ Libre Bruxelles Lab Biol Anim & Cellulaire B-1050 Brussels Belgium Fonds Natl Rech Sci B-1050 Brussels Belgium

The present-day geographic distribution of individual species of five taxonomic groups (plants, dragonflies, butterflies, herpetofauna and breeding birds) is relatively well-known on a small scale (5x5 km squares) in Flanders (north Belgium). These data allow identification of areas with a high diversity within each of the species groups. However, differences in mapping intensity and coverage hamper straightforward comparisons of species-rich areas among the taxonomic groups. To overcome this problem, we modelled the species richness of each taxonomic group separately using various environmental characteristics as predictor variables (area of different land use types, biotope diversity, topographic and climatic features). We applied forward stepwise multiple regression to build the models, using a subset of well-surveyed squares. A separate set of equally well-surveyed squares was used to test the predictions of the models. The coincidence of geographic areas with high predicted species richness was remarkably high among the four faunal groups, but much lower between plants and each of the four faunal groups. Thus, the four investigated faunal groups can be used as relatively good indicator taxa for one another in Flanders, at least for their within-group species diversity. A mean predicted species diversity per mapping square was also estimated by averaging the standardised predicted species richness over the five taxonomic groups, to locate the regions that were predicted as being the most species-rich for all five investigated taxonomic groups together. Finally, the applicability of predictive modelling in nature conservation policy both in Flanders and in other regions is discussed.

关键词： breeding birds butterflies conservation priorities dragonflies Flanders herpetofauna hotspots plants predictive modelling species richness coincidence

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The use of diachronic spatial approaches and predictive modelling to study the vegetation dynamics of a managed heathland

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BIODIVERSITY AND CONSERVATION 2011年第1期20卷 73-88页

作者： Mobaied, Samira Riera, Bernard Lalanne, Arnault Baguette, Michel Machon, Nathalie MNHN CNRS UMR 7179 Museum Natl Hist Nat F-91800 Brunoy France MNHN CNRS UMR 7179 Museum Natl Hist Nat F-91800 Brunoy France

According to the EU Habitats Directive, heathlands are a semi-natural habitat type of community interest. This status aims at conserving these habitats, especially where and when they are threatened by various changes, including natural vegetation succession. We present results of a study of the dynamics of a typical dry heathland plot located in the Fontainebleau massif (France). An exhaustive observation of vegetation changes were made on this area of four hectares between 2000 and 2008, employing a spatial approach. We recorded the expansion of Molinia caerulea (L.) Moench at the expense of Ericaceae. The potential future vegetation of the site was modelled using Markov chains coupled to a GIS programme. This model predicted a gradual change in the floristic composition of heathland in favour of M. caerulea at the expense of Calluna vulgaris (L.) Hull and Erica tetralix L., and the expansion of Pinus sylvestris L. The study demonstrates how spatial methods can contribute to the design of reliable management methods of habitats such as the heathlands.

关键词： Heathlands Vegetation dynamics Molinia caerulea Calluna vulgaris GIS predictive modelling

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Sparse grids: a new predictive modelling method for the analysis of geographic data

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INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 2005年第3期19卷 267-292页

作者： Laffan, SW Nielsen, OM Silcock, H Hegland, M Univ New S Wales Sch Biol Earth & Environm Sci Ctr Remote Sensing & GIS Sydney NSW 2052 Australia Australian Natl Univ Inst Math Sci Canberra ACT 0200 Australia

We introduce in this paper a new predictive modelling method to analyse Geographic data known as sparse grids. The sparse grids method has been developed for data-mining applications. It is a machine-learning approach to data analysis and has great applicability to the analysis and understanding of geographic data and processes. Sparse gi grids are a subset of grid-based predictive modelling approaches. The advantages they have over other grid-based methods are that they use fewer parameters and are less susceptible to the curse of dimensionality. These mean that they can be applied to many geographic problems and are readily adapted to the analysis of geographically local samples. We demonstrate the utility of the sparse grids system using a large and spatially extensive data set of regolith samples from Weipa, Australia. We apply both global and local analyses to find relationships between the regolith data and a set of geomorphometric, hydrologic and spectral variables. The results of the global analyses are much better than those generated using an artificial neural network, and the local analysis results are better than those generated using moving window regression for the same analysis window size. The sparse grids system provides a potentially powerful tool for the analysis and understanding of geographic processes and relationships.

关键词： spatial analysis geographic data predictive modelling sparse grids bauxite

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A novel area of predictive modelling: describing the functionality of beneficial microorganisms in foods

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INTERNATIONAL JOURNAL OF FOOD MICROBIOLOGY 2002年第2-3期73卷 251-259页

作者： Leroy, F Degeest, B De Vuyst, L Free Univ Brussels Dept Appl Biol Sci IMDO Res Grp Ind Microbiol Fermentat Technol & Downstr B-1050 Brussels Belgium

predictive microbiology generally focuses on the potential outgrowth of spoilage bacteria and foodborne pathogens in foods. Little attention has been paid to the biokinetics of beneficial foodgrade microorganisms, such as lactic acid bacteria. The latter is commonly used in the food fermentation industry, mainly for the in situ production of the antimicrobial lactic acid to extend the shelf life of the food. Furthermore, many strains show additional industrial potential as novel starter cultures since they produce functional metabolites, such as bacteriocins and exopolysaccharides. The production of these functional metabolites has been demonstrated during in vitro experiments, but in many cases these novel starter cultures seem to be less efficient when applied in a food system. A modelling approach may contribute to a better understanding of the tight relation between the food environment and bacterial functionality. Primary modelling can be applied to fit the experimental data concerning cell growth, sugar metabolism, and the production of functional metabolites for a given set of environmental conditions. This led to conclusions concerning the growth-associated production of bacteriocin and exopolysaccharides, the inactivation of these molecules when cell growth levels off, and a minimum cell concentration to trigger on bacteriocin production. Examples deal with the production of the bacteriocin sakacin K by the natural fermented sausage isolate Lactobacillus sakei CTC 494, and the production of heteropolysaccharides by the yoghurt starter culture Streptococcus thermophilus LY03. Secondary modelling of biokinetic parameters quantifies the production of bacteriocin and exopolysaccharides in function of environmental factors. As an example, the specific bacteriocin production by Lb. sakei CTC 494 decreases with increasing sodium chloride concentrations. Furthermore, since the assessment of functionality is frequently hampered by the nature of the food system, math

关键词： lactic acid bacteria novel starter cultures predictive modelling bacteriocin sakacin exopolysaccharides Lactobacillus sakei Streptococcus thermophilus

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Classification of macroinvertebrate communities across drainage basins in Victoria, Australia: consequences of sampling on a broad spatial scale for predictive modelling

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FRESHWATER BIOLOGY 1999年第2期41卷 253-268页

作者： Marchant, R Hirst, A Norris, R Metzeling, L Museum Victoria Abbotsford Vic 3067 Australia Univ Canberra Cooperat Res Ctr Freshwater Ecol Canberra ACT 2601 Australia Environm Protect Author Victoria Melbourne Vic 3000 Australia

1. Spatial scale may influence the interpretation of environmental gradients that underlie classification and ordination;analyses of lotic macroinvertebrate communities. This could have important consequences for the spatial scale over which predictive models derived from these multivariate analyses can be applied. 2. Macroinvertebrate community data (identified to genus or species) from edge and main-channel habitats were obtained for sites on rivers from 25 of the 29 drainage basins in Victoria. Trends in community similarity were analysed by carrying out separate multivariate analyses on data from the edge habitats (199 sites) and the main-channel habitats (163 sites). 3. Hierarchical classification (UPGMA) showed that the edge data could be placed into 11 site groups and the main-channel data into 12 site groups. 4. Ordination analysis (hybrid multidimensional scaling) showed no sharp disjunctions between site groups in either habitat;overlap was frequent. Correlation of the ordination patterns with environmental variables showed that edge communities varied longitudinally within a drainage basin and from the east to the west of Victoria. These two trends were superimposed on one another to form a single gradient on the ordination. The taxon richness of edge communities was also related to the species richness of macrophytes at a site. Main-channel communities also displayed a longitudinal and a geographic gradient, but these two gradients were uncorrelated on the ordination. 5. Community similarity only weakly reflected geographic proximity in either habitat. A preliminary subdivision of Victoria into a series of biogeographic regions did not match the pattern of distribution of site groups for the edge habitat, illustrating the difficulties of applying to lotic communities a priori regionalizations based on terrestrial features of the landscape. 6. The longitudinal gradients in the two data sets were commonly observed in data gathered at smaller spatial scales

关键词： macroinvertebrate communities drainage basins predictive modelling

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The use of predictive modelling to determine the likelihood of donor return during the COVID-19 pandemic

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TRANSFUSION MEDICINE 2024年第5期34卷 333-343页

作者： Gammon, Richard R. Hindawi, Salwa Al-Riyami, Arwa Z. Ang, Ai Leen Bazin, Renee Bloch, Evan M. Counts, Kelley de Angelis, Vincenzo Goel, Ruchika Grubovic Rastvorceva, Rada M. Pati, Ilaria Lee, Cheuk-Kwong La Raja, Massimo Mengoli, Carlo Oreh, Adaeze Patidar, Gopal Kumar Rahimi-Levene, Naomi Ravula, Usharee Rexer, Karl So-Osman, Cynthia Thachil, Jecko Nevessignsky, Michel Toungouz Vermeulen, Marion OneBlood Sci Med Tech Direct Orlando FL USA King Abdulaziz Univ Dept Hematol Jeddah Saudi Arabia Sultan Qaboos Univ Hosp Dept Hematol Muscat Oman Hlth Sci Author Blood Serv Grp Singapore Singapore Hema Quebec Med Affairs & Innovat Quebec City PQ Canada Johns Hopkins Univ Dept Pathol Transfus Med Div Sch Med Baltimore MD USA OneBlood Informat Technol Adm St Petersburg FL USA Italian Natl Inst Hlth Natl Blood Ctr Rome Italy Univ Illinois Dept Biol Springfield IL USA Inst Transfus Med RNM Skopje North Macedonia Univ Goce Delcev Fac Med Sci Stip North Macedonia Hong Kong Red Cross Blood Transfus Serv HKSAR Hong Kong Peoples R China Natl Planning Commiss Abuja Nigeria All India Inst Med Sci Dept Transfus Med New Delhi India Shamir Med Ctr Blood Bank Zerifin Israel ACS Med Coll & Hosp Dept Transfus Med Chennai India Rexer Analyt Winchester MA USA Sanquin Blood Supply Fdn Dept Transfus Med Amsterdam Netherlands Erasmus MC Dept Haematol Rotterdam Netherlands North Manchester Gen Hosp Gastroenterol Manchester England Belgian Red Cross French Speaking Serv Suarlee Belgium South African Army Coll Pretoria South Africa Univ Free State Afromontane Res Unit Phuthaditjhaba South Africa

Artificial intelligence (AI) uses sophisticated algorithms to "learn" from large volumes of data. This could be used to optimise recruitment of blood donors through predictive modelling of future blood supply, based on previous donation and transfusion demand. We sought to assess utilisation of predictive modelling and AI blood establishments (BE) and conducted predictive modelling to illustrate its use. A BE survey of data modelling and AI was disseminated to the International Society of Blood transfusion members. Additional anonymzed data were obtained from Italy, Singapore and the United States (US) to build predictive models for each region, using January 2018 through August 2019 data to determine likelihood of donation within a prescribed number of months. Donations were from March 2020 to June 2021. Ninety ISBT members responded to the survey. predictive modelling was used by 33 (36.7%) respondents and 12 (13.3%) reported AI use. Forty-four (48.9%) indicated their institutions do not utilise predictive modelling nor AI to predict transfusion demand or optimise donor recruitment. In the predictive modelling case study involving three sites, the most important variable for predicting donor return was number of previous donations for Italy and the US, and donation frequency for Singapore. Donation rates declined in each region during COVID-19. Throughout the observation period the predictive model was able to consistently identify those individuals who were most likely to return to donate blood. The majority of BE do not use predictive modelling and AI. The effectiveness of predictive model in determining likelihood of donor return was validated;implementation of this method could prove useful for BE operations.

关键词： COVID-19 blood donor predictive modelling

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Upper Palaeolithic sites and where to find them: A predictive modelling approach to assess site expectancy in the Southern Levant

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QUATERNARY INTERNATIONAL 2022年 635卷 53-72页

作者： Parow-Souchon, Hannah Zickel, Mirijam Manner, Heiko Ben Gurion Univ Negev Dept Bible Studies Archeol & Ancient Near East IL-84105 Beer Sheva Israel Univ Cologne Inst Prehist Archaeol Cologne Germany Inst Geog Otto F Str 4 D-50674 Cologne Germany

Site preservation and associated terrain characteristics are among the major agents controlling the spatial dis-tribution and discoverability of late Quaternary archaeological sites. However, site expectancy in extensive areas can be determined using a combination of geoarchaeological investigation and digital spatial analysis. In that sense a prediction model, respectively the result of a Multi Criteria Decision Analysis (MCDA), is presented to highlight areas with a high potential to contain Upper Palaeolithic sites in the Eastern Mediterranean and bordering arid margins. Based on detailed on-site analysis in the Wadi Sabra region, with its many sites and the location of other well-known sites in the southern Levant, eight individual parameters, aspect (cardinal point), elevation, geomorphology, hydrogeology, drainage network, slope inclination, vegetation, and a terrain ruggedness index (TRI) are evaluated and tested for significance. The statistical results confirm a significant divergence of the spatial distribution of Upper Palaeolithic sites from the distribution of natural terrain position factors. This allows for a definition of parameter classes with a high site expectancy and their use in a predictive model. For the results map, the site expectancy factors determined in this way were intersected with each other, thus enabling a distinction between areas with a high and low site occurrence probability. The accuracy of the presented result is assessed by the implementation of another MCDA pass based on an independent dataset of archaeological survey sites and compared to the original. Both datasets complement each other and can show the potential for further investigations with more numerous site datasets as well as more detailed spatial informa-tion. A local classification based on high-resolution elevation information and field data is evaluated for the Wadi Sabra, Southern Jordan, which can confirm yet further specify the result from the MCDA.

关键词： Upper palaeolithic GIS Multi criteria decision Analysis predictive modelling Geoarchaeology Digital archaeological survey

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predictive modelling using pathway scores: robustness and significance of pathway collections

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BMC BIOINFORMATICS 2019年第1期20卷 543-543页

作者： Segura-Lepe, Marcelo P. Keun, Hector C. Ebbels, Timothy M. D. Imperial Coll Dept Surg & Canc Computat & Syst Med Sir Alexander Fleming Bldg London SW1 2AZ England Imperial Coll London Dept Surg & Canc Div Canc Hammersmith Hosp Campus London W12 0NN England

Background Transcriptomic data is often used to build statistical models which are predictive of a given phenotype, such as disease status. Genes work together in pathways and it is widely thought that pathway representations will be more robust to noise in the gene expression levels. We aimed to test this hypothesis by constructing models based on either genes alone, or based on sample specific scores for each pathway, thus transforming the data to a 'pathway space'. We progressively degraded the raw data by addition of noise and examined the ability of the models to maintain predictivity. Results Models in the pathway space indeed had higher predictive robustness than models in the gene space. This result was independent of the workflow, parameters, classifier and data set used. Surprisingly, randomised pathway mappings produced models of similar accuracy and robustness to true mappings, suggesting that the success of pathway space models is not conferred by the specific definitions of the pathway. Instead, predictive models built on the true pathway mappings led to prediction rules with fewer influential pathways than those built on randomised pathways. The extent of this effect was used to differentiate pathway collections coming from a variety of widely used pathway databases. Conclusions Prediction models based on pathway scores are more robust to degradation of gene expression information than the equivalent models based on ungrouped genes. While models based on true pathway scores are not more robust or accurate than those based on randomised pathways, true pathways produced simpler prediction rules, emphasizing a smaller number of pathways.

关键词： Pathways Robustness predictive modelling

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