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Space-time disease mapping by combining Bayesian maximum entropy and Kalman filter: the BME-Kalman approach / Bisong Hu in International journal of geographical information science IJGIS, vol 35 n° 3 (March 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 3 (March 2021) . - pp 466 - 489 Titre : Space-time disease mapping by combining Bayesian maximum entropy and Kalman filter: the BME-Kalman approach Type de document : Article/Communication Auteurs : Bisong Hu, Auteur ; Pan Ning, Auteur ; Yi Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 466 - 489 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] carte sanitaire [Termes IGN] Chine [Termes IGN] entropie maximale [Termes IGN] filtre de Kalman [Termes IGN] géostatistique [Termes IGN] modèle dynamique [Termes IGN] régression Résumé : (auteur) In this work, a synthesis of the Bayesian maximum entropy (BME) and the Kalman filter (KF) methods, which enhances their individual strengths and overcomes certain of their weaknesses for spatiotemporal mapping purposes, is proposed in a spatiotemporal disease mapping context. The proposed BME-Kalman synthesis allows BME to use information from both parametric regression modeling and KF estimation leading to enhanced knowledge bases. The BME-Kalman synthetic approach is used to study the space-time incidence mapping of the hand, foot and mouth disease (HFMD) in Shandong province (China) during the period May 1st, 2008 to March 19th, 2009. The results showed that the BME-Kalman approach exhibited very good regressive and predictive accuracies, maintained a very good performance even during low-incidence and extremely low-incidence periods, offered an improved description of hierarchical disease characteristics compared to traditional mapping techniques, and provided a clear explanation of the spatial stratified incidence heterogeneity at unsampled locations. The BME-Kalman approach is versatile and flexible so that it can be modified and adjusted according to the needs of the application. Numéro de notice : A2021-165 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1795177 date de publication en ligne : 22/07/2021 En ligne : https://doi.org/10.1080/13658816.2020.1795177 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97098 [article]

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Urban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB / Mahya Norallahi in Natural Hazards, vol 106 n° 1 (March 2021)  

Public [article]  inNatural Hazards > vol 106 n° 1 (March 2021) . - pp119 - 137 Titre : Urban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB Type de document : Article/Communication Auteurs : Mahya Norallahi, Auteur ; Hesam Seyed Kaboli, Auteur Année de publication : 2021 Article en page(s) : pp119 - 137 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] algorithme génétique [Termes IGN] apprentissage automatique [Termes IGN] cartographie des risques [Termes IGN] classification bayesienne [Termes IGN] classification par forêts aléatoires [Termes IGN] entropie maximale [Termes IGN] inondation [Termes IGN] Iran [Termes IGN] zone urbaine Résumé : (auteur) Rapid urban development, increasing impermeable surfaces, poor drainage system and changes in extreme precipitations are the most important factors that nowadays lead to increased urban flooding and it has become an urban problem. Urban flood mapping and its use in making an urban development plan can reduce flood damages and losses. Constantly producing urban flood hazard maps using models that rely on the availability of detailed hydraulic-hydrological data is a major challenge especially in developing countries. In this study, urban flood hazard map was produced with limited data using three machine learning models: Genetic Algorithm Rule-Set Production, Maximum Entropy (MaxEnt), Random Forest (RF) and Naïve Bayes for Kermanshah city, Iran. The flood hazard predicting factors used in modeling were: slope, land use, precipitation, distance to river, distance to channel, curve number (CN) and elevation. Flood inventory map was produced based on available reports and field surveys, that 117 flooded points and 163 non-flooded points were identified. Models performance was evaluated based on area under the receiver-operator characteristic curve (AUC-ROC), Kappa statistic and hits and miss analysis. The results show that RF model (AUC-ROC = 99.5%, Kappa = 98%, Accuracy = 90%, Success ratio = 99%, Threat score = 90% and Heidke skill score = 98%) performed better than other models. The results also showed that distance to canal, land use and CN have shown more contribution among others for modeling the flood and precipitation had the least effect among other factors. The findings show that machine learning methods can be a good alternative to distributed models to predict urban flood-prone areas where there are lack of detailed hydraulic and hydrological data. Numéro de notice : A2021-418 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s11069-020-04453-3 date de publication en ligne : 04/01/2021 En ligne : https://doi.org/10.1007/s11069-020-04453-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97768 [article]

Spatial characterization and distribution modelling of Ensete ventricosum (wild and cultivated) in Ethiopia / Meron Awoke Eshetae in Geocarto international, vol 36 n° 1 ([01/01/2021])  

Public [article]  inGeocarto international > vol 36 n° 1 [01/01/2021] . - pp 60 - 75 Titre : Spatial characterization and distribution modelling of Ensete ventricosum (wild and cultivated) in Ethiopia Type de document : Article/Communication Auteurs : Meron Awoke Eshetae, Auteur ; Binyam Tesfaw Hailu, Auteur ; Sebsebe Demissew, Auteur Année de publication : 2021 Article en page(s) : pp 60 - 75 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] distribution spatiale [Termes IGN] données de terrain [Termes IGN] données environnementales [Termes IGN] entropie maximale [Termes IGN] Ethiopie [Termes IGN] Musa (genre) [Termes IGN] surface cultivée Résumé : (Auteur) Enset (Ensete ventricosum) feeds around 20 million people in Ethiopia and is arguably the most important crop for food security and rural livelihoods in the country. Therefore, it is significant to know its spatial characterization and distribution in the country. We use spatial overlay analysis and the maximum entropy (MaxEnt) model for characterizing and modelling, respectively. Inputs for the model include 26 environmental variables—19 bioclimatic and seven biophysical—in addition to the geospatial location of enset field data. The model result was validated using Receiver Operating Characteristic curve method and the area under the curve (AUC) with 0.842 for cultivated enset and 0.760 (wild enset). The highest prediction (>0.5) of both varieties occurred in the southwestern, south-central and north-eastern parts of Ethiopia—17,293.67 km2 (cultivated) and 40,402 km2 (wild) area. The presence of both enset is probabilistically higher at the tropic-cool/sub-humid Agroecological Zones. Numéro de notice : A2021-051 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1588392 date de publication en ligne : 10/06/2020 En ligne : https://doi.org/10.1080/10106049.2019.1588392 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96773 [article]

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How do species and data characteristics affect species distribution models and when to use environmental filtering? / Lukáš Gábor in International journal of geographical information science IJGIS, vol 34 n° 8 (August 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 8 (August 2020) . - pp 1567 - 1584 Titre : How do species and data characteristics affect species distribution models and when to use environmental filtering? Type de document : Article/Communication Auteurs : Lukáš Gábor, Auteur ; Vítězslav Moudrý, Auteur ; Vojtěch Barták, Auteur ; Vincent Lecours, Auteur Année de publication : 2020 Article en page(s) : pp 1567 - 1584 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] distribution spatiale [Termes IGN] données environnementales [Termes IGN] données localisées [Termes IGN] échantillonnage (statistique) [Termes IGN] entropie maximale [Termes IGN] erreur d'échantillon [Termes IGN] filtrage d'information [Termes IGN] interaction spatiale Résumé : (auteur) Species distribution models (SDMs) are widely used in ecology and conservation. However, their performance is known to be affected by a variety of factors related to species occurrence characteristics. In this study, we used a virtual species approach to overcome the difficulties associated with testing of combined effects of those factors on performance of presence-only SDMs when using real data. We focused on the individual and combined roles of factors related to response variable (i.e. sample size, sampling bias, environmental filtering, species prevalence, and species response to environmental gradients). Results suggest that environmental filtering is not necessarily helpful and should not be performed blindly, without evidence of bias in species occurrences. The more gradual the species response to environmental gradients is, the greater is the model sensitivity to an inappropriate use of environmental filtering, although this sensitivity decreases with higher species prevalence. Results show that SDMs are affected to the greatest degree by the species response to environmental gradients, species prevalence, and sample size. Models’ accuracy decreased with sample size below 300 presences. Furthermore, a high level of interactions among individual factors was observed. Ignoring the combined effects of factors may lead to misleading outcomes and conclusions. Numéro de notice : A2020-414 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1615070 date de publication en ligne : 14/05/2019 En ligne : https://doi.org/10.1080/13658816.2019.1615070 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95465 [article]

Landslide susceptibility mapping using maximum entropy and support vector machine models along the highway corridor, Garhwal Himalaya / Vijendra Kumar Pandey in Geocarto international, vol 35 n° 2 ([01/02/2020])  

Public [article]  inGeocarto international > vol 35 n° 2 [01/02/2020] . - pp 168 - 187 Titre : Landslide susceptibility mapping using maximum entropy and support vector machine models along the highway corridor, Garhwal Himalaya Type de document : Article/Communication Auteurs : Vijendra Kumar Pandey, Auteur ; Hamid Reza Pourghasemi, Auteur ; Milap Chand Sharma, Auteur Année de publication : 2020 Article en page(s) : pp 168 - 187 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] autoroute [Termes IGN] classification dirigée [Termes IGN] effondrement de terrain [Termes IGN] entropie maximale [Termes IGN] Himalaya [Termes IGN] image IRS-LISS [Termes IGN] image Landsat-8 [Termes IGN] Linear Imaging Self-Scanning System [Termes IGN] modèle statistique [Termes IGN] mousson [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] séparateur à vaste marge [Termes IGN] test statistique Résumé : (Auteur) The main objective of this study to produce landslide susceptibility zones using maximum entropy (MaxEnt) and support vector machine (SVM) data-driven models along the Tipari to Ghuttu highway corridors in the Garhwal Himalaya. A landslide inventory has been prepared through field surveys and LISS-IV and Landsat 8 satellite images. The datasets of 85 landslides were categorised into training and test sets. In this study 11 landslide conditioning variables were used that are; altitude, slope angle, aspect, plan curvature, topographic wetness index, normalised difference vegetation index (NDVI), land use, soil texture, distance to rivers, distance to faults, and distance to the road. The result produced using MaxEnt and SVM model were subsequently validated using receiver operating characteristics curve (ROC) with test sets of landslide dataset. Both the models have good prediction capabilities. MaxEnt has ROC value of 0.78 while SVM has the highest prediction rate of 0.85. Numéro de notice : A2020-036 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1510038 date de publication en ligne : 20/09/2018 En ligne : https://doi.org/10.1080/10106049.2018.1510038 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94519 [article]

Combining land cover products using a minimum divergence and a Bayesian data fusion approach / Sarah Gengler in International journal of geographical information science IJGIS, vol 32 n° 3-4 (March - April 2018)  

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Development and Comparison of Species Distribution Models for Forest Inventories / Óscar Rodríguez de Rivera in ISPRS International journal of geo-information, vol 6 n° 6 (June 2017)  

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Climatic niche breadth can explain variation in geographical range size of alpine and subalpine plants / Fangyuan Yu in International journal of geographical information science IJGIS, vol 31 n° 1-2 (January - February 2017)  

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Introduction to Artificial Intelligence / Wolfgang Ertel (2017)  

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An entropy-based multispectral image classification algorithm / Di Long in IEEE Transactions on geoscience and remote sensing, vol 51 n° 12 (December 2013)  

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