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Identifying urban neighborhoods with higher potential for social investment using GIS-FIS approach / Hossein Aghajani in Applied geomatics, vol 13 n° 1 (May 2021)  

Public [article]  inApplied geomatics > vol 13 n° 1 (May 2021) . - pp 1 - 13 Titre : Identifying urban neighborhoods with higher potential for social investment using GIS-FIS approach Type de document : Article/Communication Auteurs : Hossein Aghajani, Auteur ; Ali Alizadeh-Zoeram, Auteur Année de publication : 2021 Article en page(s) : pp 1 - 13 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] analyse spatiale [Termes IGN] ArcGIS [Termes IGN] distribution spatiale [Termes IGN] gestion urbaine [Termes IGN] Inférence floue [Termes IGN] Iran [Termes IGN] Matlab [Termes IGN] politique sociale [Termes IGN] processus d'analyse hiérarchique [Termes IGN] quartier [Termes IGN] système d'information géographique Résumé : (auteur) Today, participation in social investment is a critical concern of urban management in metropolitan areas. To be effective in both public and private sectors, such participation requires an accurate knowledge of geographical distribution of resources and social needs in different parts of the city in order to better manage these investments. To this end, the present research aimed at providing an objective and tangible integrated approach to identify urban neighborhoods with high potential for social investment. The research area was neighborhoods located in Mashhad, Iran. Thirty-one criteria in three dimensions of empowerment, education, and culture were identified. Having determined, normalized, and weighted the values of each criteria in different neighborhoods using the analytic hierarchy process (AHP) method, the weight scores of each neighborhood in each dimension were added up by GIS approach and Arc GIS 10.1 software. Then, in order to conclude the overall status of social investment in different neighborhoods, a fuzzy inference system (FIS) was designed in the MATLAB 2014a environment according to experts’ opinions. Finally, based on the output of the designed system, the overall status of each neighborhood studied was determined in terms of social investment and neighborhoods with high potential for social investment were identified. The approach presented provides an insight for urban mangers, companies, and philanthropic investors to invest more effectively given the appropriate social investment opportunities. Numéro de notice : A2021-236 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s12518-020-00317-4 date de publication en ligne : 27/05/2020 En ligne : https://doi.org/10.1007/s12518-020-00317-4 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97243 [article]

Application of fuzzy analytical hierarchy process for assessment of desertification sensitive areas in North West of Morocco / Hicham Ait Kacem in Geocarto international, vol 36 n° 5 ([15/03/2021])  

Public [article]  inGeocarto international > vol 36 n° 5 [15/03/2021] . - pp 563 - 580 Titre : Application of fuzzy analytical hierarchy process for assessment of desertification sensitive areas in North West of Morocco Type de document : Article/Communication Auteurs : Hicham Ait Kacem, Auteur ; Saïd Fal, Auteur ; Mounir Karim, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 563 - 580 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] analyse de sensibilité [Termes IGN] analyse multicritère [Termes IGN] carte de la végétation [Termes IGN] climat méditerranéen [Termes IGN] désertification [Termes IGN] Inférence floue [Termes IGN] Maroc [Termes IGN] processus d'analyse hiérarchisée floue [Termes IGN] zone semi-aride Résumé : (auteur) An updated assessment of land sensitivity to desertification is essential for comprehensive planning and preparation of action plans. In this study, modified Mediterranean desertification and land use (MEDALUS) method by employing a fuzzy inference system in a GIS environment was used to identify environmentally sensitive areas (ESA) to desertification in the Drader basin in Morocco. Anthropogenic and Biophysical parameters (17 parameters) have been integrated and processed by geographic information systems obtaining climate, water, soil, vegetation and land management quality indexes. The weights for indicators and parameters involved in land desertification were calculated by using analytical hierarchy process (AHP). Then, a fuzzy inference system was employed to create a map for each indexes and ESA map for the study area. The results showed that, fragile and critical areas represent 58% and 2% respectively, while about 21% of the area belongs to the potential areas. The critical areas are primarily characterized by low degree of plant cover, low erosion resistance and low management quality. However, the results revealed that Integrating MCDM-GIS and fuzzy logic in MEDALUS has improved the ability to classify the sensitive areas to desertification at regional scale. The ESA maps have potential as decision tools to prepare and respond to ESAs and causes of land degradation, as such contributing to national climate action and disaster risk reduction sustainable development goals (goals 13 and 15, respectively). Numéro de notice : A2021-252 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/10106049.2019.1611949 date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1611949 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97274 [article]

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Bistatic specular scattering measurements for the estimation of rice crop growth variables using fuzzy inference system at X-, C-, and L-bands / Ajeet Kumar Vishwakarma in Geocarto international, vol 35 n° 13 ([01/10/2020])  

Public [article]  inGeocarto international > vol 35 n° 13 [01/10/2020] . - pp 1433 - 1449 Titre : Bistatic specular scattering measurements for the estimation of rice crop growth variables using fuzzy inference system at X-, C-, and L-bands Type de document : Article/Communication Auteurs : Ajeet Kumar Vishwakarma, Auteur ; Rajendra Prasad, Auteur Année de publication : 2020 Article en page(s) : pp 1433 - 1449 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] bande C [Termes IGN] bande L [Termes IGN] bande X [Termes IGN] biomasse [Termes IGN] indice foliaire [Termes IGN] Inférence floue [Termes IGN] Leaf Area Index [Termes IGN] Oryza (genre) [Termes IGN] polarisation [Termes IGN] radar bistatique [Termes IGN] teneur en eau de la végétation Résumé : (auteur) Bistatic scatterometer measurements were performed on the rice crop-bed in the angular range of 20° to 60° for specular direction (ϕ=0) at X-, C- and L-bands for HH-, VV-, and HV-polarizations. The dominant scattering contribution to bistatic specular scattering coefficients (σ0) was analysed with the crop growth stages at various angle of incidence. The regression analysis showed high correlation between σ0 and crop growth variables at 40° angle of incidence for HH-polarization at X-band and for VV-polarization at C- and L-bands. The estimation of rice crop growth variables using subtractive clustering based fuzzy inference system (S-FIS) was done at 40° angle of incidence. The lower values of computed root mean square error (RMSE) between the observed and estimated values showed high potential of developed S-FIS model for the estimation of leaf area index for HH-polarisation at X-band, vegetation water content and fresh biomass for VV-polarization at C- and L-bands, respectively. Numéro de notice : A2020-608 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1576777 date de publication en ligne : 18/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1576777 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95969 [article]

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Mangrove forest classification and aboveground biomass estimation using an atom search algorithm and adaptive neuro-fuzzy inference system / Minh Hai Pham in Plos one, vol 15 n° 5 (May 2020)  

Public [article]  inPlos one > vol 15 n° 5 (May 2020) . - n° 0233110 Titre : Mangrove forest classification and aboveground biomass estimation using an atom search algorithm and adaptive neuro-fuzzy inference system Type de document : Article/Communication Auteurs : Minh Hai Pham, Auteur ; Thi Hoai Do, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 0233110 Note générale : biblographie Langues : Anglais (eng) Descripteur : [Termes IGN] algorithme de recherche [Termes IGN] biomasse aérienne [Termes IGN] biomasse forestière [Termes IGN] changement d'occupation du sol [Termes IGN] image Sentinel-SAR [Termes IGN] image SPOT 6 [Termes IGN] Inférence floue [Termes IGN] mangrove [Termes IGN] Viet Nam [Vedettes matières IGN] Ecologie forestière Résumé : (auteur) Background : Advances in earth observation and machine learning techniques have created new options for forest monitoring, primarily because of the various possibilities that they provide for classifying forest cover and estimating aboveground biomass (AGB). Methods : This study aimed to introduce a novel model that incorporates the atom search algorithm (ASO) and adaptive neuro-fuzzy inference system (ANFIS) into mangrove forest classification and AGB estimation. The Ca Mau coastal area was selected as a case study since it has been considered the most preserved mangrove forest area in Vietnam and is being investigated for the impacts of land-use change on forest quality. The model was trained and validated with a set of Sentinel-1A imagery with VH and VV polarizations, and multispectral information from the SPOT image. In addition, feature selection was also carried out to choose the optimal combination of predictor variables. The model performance was benchmarked against conventional methods, such as support vector regression, multilayer perceptron, random subspace, and random forest, by using statistical indicators, namely, root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). Results : The results showed that all three indicators of the proposed model were statistically better than those from the benchmarked methods. Specifically, the hybrid model ended up at RMSE = 70.882, MAE = 55.458, R2 = 0.577 for AGB estimation. Conclusion : From the experiments, such hybrid integration can be recommended for use as an alternative solution for biomass estimation. In a broader context, the fast growth of metaheuristic search algorithms has created new scientifically sound solutions for better analysis of forest cover. Numéro de notice : A2020-833 Affiliation des auteurs : non IGN Thématique : FORET/INFORMATIQUE Nature : Article DOI : https://doi.org/10.1371/journal.pone.0233110 date de publication en ligne : 21/05/2020 En ligne : https://doi.org/10.1371/journal.pone.0233110 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97667 [article]

Wavelet-adaptive neural subtractive clustering fuzzy inference system to enhance low-cost and high-speed INS/GPS navigation system / Elahe S. Abdolkarimi in GPS solutions, vol 24 n° 2 (April 2020)  

Public [article]  inGPS solutions > vol 24 n° 2 (April 2020) Titre : Wavelet-adaptive neural subtractive clustering fuzzy inference system to enhance low-cost and high-speed INS/GPS navigation system Type de document : Article/Communication Auteurs : Elahe S. Abdolkarimi, Auteur ; Mohammad-Reza Mosavi, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes IGN] centrale inertielle [Termes IGN] coût [Termes IGN] filtre de Kalman [Termes IGN] GPS-INS [Termes IGN] imprécision des données [Termes IGN] incertitude des données [Termes IGN] Inférence floue [Termes IGN] précision du positionnement [Termes IGN] rapport signal sur bruit [Termes IGN] transformation en ondelettes [Termes IGN] vitesse de déplacement Résumé : (auteur) The combined navigation system consisting of Global Positioning System (GPS) and Inertial Navigation System in a complementary mode assures an accurate, reliable, and continuous positioning capability in the navigation system. Because of problems such as dealing with a low-cost MEMS-based inertial sensors having a high level of uncertainty and imprecision, stochastic noise, a high-speed vehicle, high noisy real data, and long-term GPS signal outage during the real-time flight test, the advantage is taken for some approaches in different steps: (1) utilizing discrete wavelet transform technique to enhance the signal-to-noise ratio in raw and noisy inertial sensor signals and attenuate high-frequency noise as a preprocessing phase to prepare more accurate data for the proposed model and (2) employing adaptive neural subtractive clustering fuzzy inference system (ANSCFIS) which combines and extracts the best feature of adaptive neuro-fuzzy inference system (ANFIS), and the subtractive clustering algorithm with fewer rules than the ANFIS method, aiming to improve a more efficient, accurate, and especially a faster method which enhances the prediction accuracy and speeds up the positioning system. The achieved accuracies for the proposed model are discussed and compared with the extended Kalman filter (EKF), ANFIS, and ANSCFIS which are implemented and tested experimentally using a high-speed vehicle in three GPS blockages. The proposed model shows considerable improvements in high-speed navigation using low-cost MEMS-based inertial sensors in case of long-term GPS blockage. Numéro de notice : A2020-084 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-020-0951-y date de publication en ligne : 11/01/2020 En ligne : https://doi.org/10.1007/s10291-020-0951-y Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94654 [article]

Automated extraction of lane markings from mobile LiDAR point clouds based on fuzzy inference / Heidar Rastiveis in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)  

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Spatial distribution of coal quality parameters with respect to production requirements: an adaptive neuro-fuzzy application for the Can coal field (Turkey) / Ali Kayabasi in Geocarto international, vol 31 n° 1 - 2 (January - February 2016)  

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Fuzzy inference guided cellular automata urban-growth modelling using multi-temporal satellite images / S. Al-Kheder in International journal of geographical information science IJGIS, vol 22 n°11-12 (november 2008)

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