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A comparative approach of support vector machine kernel functions for GIS-based landslide susceptibility mapping / Khalil Valizadeh Kamran in Applied geomatics, vol 13 n° 4 (December 2021)  

Public [article]  inApplied geomatics > vol 13 n° 4 (December 2021) . - pp 837 - 851 Titre : A comparative approach of support vector machine kernel functions for GIS-based landslide susceptibility mapping Type de document : Article/Communication Auteurs : Khalil Valizadeh Kamran, Auteur ; Bakhtiar Feizizadeh, Auteur ; Behnam Khorrami, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 837 - 851 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de sensibilité [Termes IGN] apprentissage automatique [Termes IGN] cartographie des risques [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] effondrement de terrain [Termes IGN] fonction de base radiale [Termes IGN] Iran [Termes IGN] méthode fondée sur le noyau [Termes IGN] occupation du sol [Termes IGN] pente [Termes IGN] risque naturel [Termes IGN] système d'information géographique [Termes IGN] utilisation du sol Résumé : (auteur) Landslides are among the most destructive natural hazards with severe socio-economic ramifications all around the world. Understanding the critical combination of geoenvironmental factors involved in the occurrence of landslides can mitigate the adverse impacts ascribed to them. Among the several scenarios for studying and investigating this phenomenon, landslide susceptibility mapping (LSM) is the most prominent method. Applying the machine learning (ML) algorithms integrated with the geographic information systems (GIS) has become a trending means for accurate and rapid landslide mapping practices in the scientific community. Support vector machine (SVM) has been the most commonly applied ML algorithm for LSM in recent years. The current study aims to implement different SVM kernel functions including polynomial kernel function (PKF) (degree 1 to 5), radial basis function (RBF), sigmoid, and linear kernels, for a GIS-based LSM over the Tabriz Basin (TB). To this end, a total number of 9 conditioning parameters being involved in the occurrence of the landslide events were determined and utilized. The LSM maps of the TB were generated based on the different SVM kernels and were statistically validated according to the landslide inventory. The findings revealed that the polynomial-degree-2 (PKF-2) model (AUC = 0.9688) outperforms the rest of the utilized kernels. According to the SLM map generated through PKF-2, the northernmost parts of the TB are extremely susceptible to slope failures than the rest; therefore, the developmental policies over these parts have to be taken into account with privileged priority to hinder any humanitarian as well as environmental catastrophes. Numéro de notice : A2021-858 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s12518-021-00393-0 Date de publication en ligne : 28/08/2021 En ligne : https://doi.org/10.1007/s12518-021-00393-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99066 [article]

Estimation of some stand parameters from textural features from WorldView-2 satellite image using the artificial neural network and multiple regression methods: a case study from Turkey / Alkan Günlü in Geocarto international, vol 36 n° 8 ([01/05/2021])  

Public [article]  inGeocarto international > vol 36 n° 8 [01/05/2021] . - pp 918 - 935 Titre : Estimation of some stand parameters from textural features from WorldView-2 satellite image using the artificial neural network and multiple regression methods: a case study from Turkey Type de document : Article/Communication Auteurs : Alkan Günlü, Auteur ; İlker Ercanlı, Auteur ; Muammer Şenyurt, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 918 - 935 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] biomasse aérienne [Termes IGN] classification par Perceptron multicouche [Termes IGN] échantillonnage [Termes IGN] fonction de base radiale [Termes IGN] gestion forestière [Termes IGN] image proche infrarouge [Termes IGN] image Worldview [Termes IGN] matrice de co-occurrence [Termes IGN] peuplement forestier [Termes IGN] Pinus nigra [Termes IGN] régression multiple [Termes IGN] réseau neuronal artificiel [Termes IGN] texture d'image [Termes IGN] Turquie Résumé : (auteur) The aim of this research is to assess some stand parameters such as stand volume (SV), basal area (BA), number of trees (NT) and aboveground biomass (AGB) of pure Crimean pine forest stands in Turkey by using ground measurements and remote sensing techniques. For this purpose, 86 sample plots were collected from pure Crimean pine stands of Yenice Forest Management Planning Unit in Ilgaz Forest Management Enterprise, Turkey. The stand parameters of each sample area were estimated using the data obtained from the sample plots. Subsequently, we calculated the values of contrast (CON), correlation (COR), dissimilarity (DIS), entropy (ENT), homogeneity (HOM), mean (M), second moment (SM) and variance (VAR) from WorldView-2 imagery using a grey-level co-occurrence matrix method. Eight textural features and twelve different window sizes ranging from 3 × 3 to 25 × 25 were generated from blue, green, red and near-infrared bands of the WorldView-2 satellite image. For predicting the relationships between WorldView-2 textural features and stand parameters of each sample plot, regression models were developed by using multiple linear regression (MLR) analysis. Additionally, artificial neural networks (ANNs) based on the multilayer perceptron (MLP) and the radial basis function (RBF) architectures were trained by comparing various numbers of neurons and activation functions in their network types. The results showed that the MLR models had low the coefficient of determination (R2) values (0.32 for SV, 0.35 for BA, 0.33 for NT and 0.34 for AGB), and the most of the ANNs models (MLP and RBF) were better than the regression models for estimating stand parameters. The ANNs model containing MLP and RBF for SV (R2 = 0.40; R2 = 0.56), for BA (R2 = 0.34; R2 = 0.51), for NT (R2 = 0.34; R2 = 0.37) and for AGB (R2 = 0.34, R2 = 0.57) were found the best results, respectively. Our results revealed that the ANNs models developed with WorldView-2 satellite image were beneficial to estimate stand parameters better than the MLR model in pure Crimean pine stands. Numéro de notice : A2021-484 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1629644 Date de publication en ligne : 25/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1629644 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97443 [article]

Performance evaluation of artificial neural networks for natural terrain classification / Perpetual Hope Akwensi in Applied geomatics, vol 13 n° 1 (May 2021)  

Public [article]  inApplied geomatics > vol 13 n° 1 (May 2021) Titre : Performance evaluation of artificial neural networks for natural terrain classification Type de document : Article/Communication Auteurs : Perpetual Hope Akwensi, Auteur ; Eric Thompson Brantson, Auteur ; Johanna Ngula Niipele, Auteur ; et al., Auteur Année de publication : 2021 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Afrique occidentale [Termes IGN] classification par nuées dynamiques [Termes IGN] échantillonnage [Termes IGN] fonction de base radiale [Termes IGN] image Landsat-OLI [Termes IGN] image multibande [Termes IGN] inventaire de la végétation [Termes IGN] réalité de terrain [Termes IGN] regroupement de données [Termes IGN] réseau neuronal artificiel [Termes IGN] segmentation d'image Résumé : (auteur) Remotely sensed image segmentation and classification form a very important part of remote sensing which involves geo-data processing and analysis. Artificial neural networks (ANNs) are powerful machine learning approaches that have been successfully implemented in numerous fields of study. There exist many kinds of neural networks and there is no single efficient approach for resolving all geospatial problems. Therefore, this research aims at investigating and evaluating the efficiency of three ANN approaches, namely, backpropagation neural network (BPNN), radial basis function neural network (RBFNN), and Elman backpropagation recurrent neural network (EBPRNN) using multi-spectral satellite images for terrain feature classification. Additionally, there has been close to no application of EBPRNN in modeling multi-spectral satellite images even though they also contain patterns. The efficiency of the three tested approaches is presented using the kappa coefficient, user’s accuracy, producer’s accuracy, overall accuracy, classification error, and computational simulation time. The study demonstrated that all the three ANN models achieved the aim of pattern identification, segmentation, and classification. This paper also discusses the observations of increasing sample sizes as inputs in the various ANN models. It was concluded that RBFNN’s computational time increases with increasing sample size and consequently increasing the number of hidden neurons; BPNN on overall attained the highest accuracy compared to the other models; EBPRNN’s accuracy increases with increasing sample size, hence a promising and perhaps an alternative choice to BPNN and RBFNN if very large datasets are involved. Based on the performance metrics used in this study, BPNN is the best model out of the three evaluated ANN models. Numéro de notice : A2021-223 Affiliation des auteurs : non IGN Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s12518-021-00360-9 Date de publication en ligne : 13/02/2021 En ligne : https://doi.org/10.1007/s12518-021-00360-9 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97194 [article]

Polynomials: special polynomials and number-theoretical applications / Ákos Pintér (2021)  

Public Titre : Polynomials: special polynomials and number-theoretical applications Type de document : Monographie Auteurs : Ákos Pintér, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2021 Importance : 154 p. Format : 17 x 25 cm ISBN/ISSN/EAN : 978-3-0365-0819-1 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Mathématique [Termes IGN] distribution binomiale [Termes IGN] équation polynomiale [Termes IGN] fonction de base radiale [Termes IGN] formule d'Euler [Termes IGN] interpolation polynomiale [Termes IGN] théorème de Bernstein [Termes IGN] trigonométrie Résumé : (éditeur) Polynomials play a crucial role in many areas of mathematics including algebra, analysis, number theory, and probability theory. They also appear in physics, chemistry, and economics. Especially extensively studied are certain infinite families of polynomials. Here, we only mention some examples: Bernoulli, Euler, Gegenbauer, trigonometric, and orthogonal polynomials and their generalizations. There are several approaches to these classical mathematical objects. This Special Issue presents nine high quality research papers by leading researchers in this field. I hope the reading of this work will be useful for the new generation of mathematicians and for experienced researchers as well. Note de contenu : 1- Two variables Shivley’s matrix polynomial 2- Some symmetric identities for degenerate Carlitz-type (p, q)-Euler numbers and polynomials 3- Symmetric identities for Carlitz-type higher-order degenerate (p, q)-Euler numbers and polynomials 4- Durrmeyer-type generalization of parametric Bernstein operators 5- A collocation method using radial polynomials for solving partial differential equations 6- On the decomposability of the linear combinations of Euler polynomials with odd degrees 7- Structure of approximate roots based on symmetric properties of (p, q)-cosine and (p, q)-sine Bernoulli polynomials 8- Explicit properties of q-cosine and q-sine Euler polynomials containing symmetric structures 9- Certain results for the twice-iterated 2D q-Appell polynomials Numéro de notice : 28632 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-0365-0819-1 En ligne : https://doi.org/10.3390/books978-3-0365-0819-1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99635

Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods / Elisa Kamir in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 160 (February 2020) . - pp 124 - 135 Titre : Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods Type de document : Article/Communication Auteurs : Elisa Kamir, Auteur ; François Waldner, Auteur ; Zvi Hochman, Auteur Année de publication : 2020 Article en page(s) : pp 124 - 135 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage automatique [Termes IGN] Australie [Termes IGN] blé (céréale) [Termes IGN] carte agricole [Termes IGN] climat [Termes IGN] estimation de précision [Termes IGN] fonction de base radiale [Termes IGN] image satellite [Termes IGN] modèle de croissance végétale [Termes IGN] modèle non linéaire [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] régression [Termes IGN] rendement agricole [Termes IGN] série temporelle [Termes IGN] variation saisonnière Résumé : (Auteur) Closing the yield gap between actual and potential wheat yields in Australia is important to meet the growing global demand for food. The identification of hotspots of the yield gap, where the potential for improvement is the greatest, is a necessary step towards this goal. While crop growth models are well suited to quantify potential yields, they lack the ability to provide accurate large-scale estimates of actual yields, owing to the sheer quantity of data they require for parameterisation. In this context, we sought to provide accurate estimates of actual wheat yields across the Australian wheat belt based on machine-learning regression methods, climate records and satellite image time series. Out of nine base learners and two ensembles, support vector regression with radial basis function emerged as the single best learner (root mean square error of 0.55 t ha−1 and R2 of 0.77 at the pixel level). At national scale, this model explained 73% of the yield variability observed across statistical units. Benchmark approaches based on peak Normalised Difference Vegetation Index (NDVI) and on a harvest index were largely outperformed by the machine-learning regression models (R2  Numéro de notice : A2020-046 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.11.008 Date de publication en ligne : 20/12/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.11.008 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94556 [article]

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