Thank you very much for downloading logistic regression a self learning text. As you may know, people have search hundreds times for their favorite books like this logistic regression a self learning text, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they are facing with some infectious bugs inside their desktop computer. logistic regression a self learning text is available in our book collection an online access to it is set as public so you can get it instantly. Our digital library spans in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the logistic regression a self learning text is universally compatible with any devices to read.

Logistic Regression A Self Learning Text

Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods

Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance

Evaluation of vertical accuracy of open source Digital Elevation Model (DEM)

Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method.

Abstract. Forest fires have devastating effects on biodiversity, climate, and humans. Producing detailed and reliable forest fire susceptibility maps is crucial for disaster management. Data-driven machine learning methods can be applied for forest fire susceptibility mapping, and learning data required for this purpose can be obtained from high-resolution satellite imagery along with a fire inventory. In this study, we assessed the performances of Random Forest (RF) and artificial neural network (ANN) classifiers for producing forest fire susceptibility maps of a region in north-east Türkiye covering Trabzon, Gümüşhane, Rize, and Bayburt provinces using freely available Earth observation data and forest inventory provided by the regional directorate. Forest type, EU-DEM v1.1 (25 m), and tree cover density were retrieved from Copernicus Land Monitoring Service. Sentinel-2 images were utilized for calculating spectral indices such as normalized difference vegetation index and modified normalized difference water index to assess surface water and vegetation characteristics. Thus, a total of twelve variables including topographic, anthropogenic, hydrologic, vegetation and land use data were used as input. The RF and ANN illustrated similar prediction performances based on receiver operating characteristics (ROC) area under the curve (AUC) values, which were 0.89 and 0.88, respectively. The RF performed better in terms of overall accuracy and F-1 score. The susceptibility maps with 25 m resolution were also investigated visually. The ANN results predicted higher susceptibility levels and larger areas were found prone to wildfire. Leave-one-out analysis results indicated that elevation was the most influential factor based on the achieved OA.


Forest fire susceptibility assessment with machine learning methods in north-east turkiye

. The automation of geoinformation (GI) collection and interpretation has been a fundamental goal for many researchers. The developments in various sensors, platforms, and algorithms have been contributing to the achievement of this goal. In addition, the contributions of citizen science (CitSci) and volunteered geographical information (VGI) concepts have become evident and extensive for the geodata collection and interpretation in the era where information has the utmost importance to solve societal and environmental problems. The web- and mobile-based Geographical Information Systems (GIS) have facilitated the broad and frequent use of GI by people from any background, thanks to the accessibility and the simplicity of the platforms. On the other hand, the increased use of GI also yielded a great increment in the demand for GI in different application areas. Thus, new algorithms and platforms allowing human intervention are immensely required for semi-automatic GI extraction to increase the accuracy. By integrating the novel artificial intelligence (AI) methods including deep learning (DL) algorithms on WebGIS interfaces, this task can be achieved. Thus, volunteers with limited knowledge on GIS software can be supported to perform accurate processing and to make guided decisions. In this study, a web-based geospatial AI (GeoAI) platform was developed for map updating by using the image processing results obtained from a DL algorithm to assist volunteers. The platform includes vector drawing and editing capabilities and employs a spatial database management system to store the final maps. The system is flexible and can utilise various DL methods in the image segmentation.

/pdf/a-webgis-framework-for-semi-automated-geodatabase-updating-w85nii0ov8.pdf

A webgis framework for semi-automated geodatabase updating assisted by deep learning

RASAT is the second Turkish Earth Observation satellite which was launched in 2011. It operates with pushbroom principle and acquires panchromatic and MS images with 7.5 m and 15 m resolutions, respectively. The swath width of the sensor is 30 km. The main aim of this study is to analyse the radiometric and geometric quality of RASAT images. A systematic validation approach for the RASAT imagery and its products is being applied. RASAT image pair acquired over Kesan city in Edirne province of Turkey are used for the investigations. The raw RASAT data (L0) are processed by Turkish Space Agency (TUBITAK-UZAY) to produce higher level image products. The image products include radiometrically processed (L1), georeferenced (L2) and orthorectified (L3) data, as well as pansharpened images. The image quality assessments include visual inspections, noise, MTF and histogram analyses. The geometric accuracy assessment results are only preliminary and the assessment is performed using the raw images. The geometric accuracy potential is investigated using 3D ground control points extracted from road intersections, which were measured manually in stereo from aerial images with 20 cm resolution and accuracy. The initial results of the study, which were performed using one RASAT panchromatic image pair, are presented in this paper.

/pdf/radiometric-and-geometric-accuracy-analysis-of-rasat-pan-20fb8uinr4.pdf

Radiometric and geometric accuracy analysis of rasat pan imagery

Abstract Preparation of accurate and up-to-date susceptibility maps at the regional scale is mandatory for disaster mitigation, site selection, and planning in areas prone to multiple natural hazards. In this study, we proposed a novel multi-hazard susceptibility assessment approach that combines expert-based and supervised machine learning methods for landslide, flood, and earthquake hazard assessments for a basin in Elazig Province, Türkiye. To produce the landslide susceptibility map, an ensemble machine learning algorithm, random forest, was chosen because of its known performance in similar studies. The modified analytical hierarchical process method was used to produce the flood susceptibility map by using factor scores that were defined specifically for the area in the study. The seismic hazard was assessed using ground motion parameters based on Arias intensity values. The univariate maps were synthesized with a Mamdani fuzzy inference system using membership functions designated by expert. The results show that the random forest provided an overall accuracy of 92.3% for landslide susceptibility mapping. Of the study area, 41.24% were found prone to multi-hazards (probability value &gt; 50%), but the southern parts of the study area are more susceptible. The proposed model is suitable for multi-hazard susceptibility assessment at a regional scale although expert intervention may be required for optimizing the algorithms. 

/pdf/a-hybrid-multi-hazard-susceptibility-assessment-model-for-a-3pdc6ya5.pdf

A Hybrid Multi-Hazard Susceptibility Assessment Model for a Basin in Elazig Province, Türkiye

/pdf/cok-yuksek-cozunurluklu-uydu-goruntulerinden-derin-ogrenme-1l3bojix.pdf

Sultan Kocaman

Papers

Forest fire susceptibility assessment with machine learning methods in north-east turkiye

A webgis framework for semi-automated geodatabase updating assisted by deep learning

Radiometric and geometric accuracy analysis of rasat pan imagery

A Hybrid Multi-Hazard Susceptibility Assessment Model for a Basin in Elazig Province, Türkiye

Çok yüksek çözünürlüklü uydu görüntüleri̇nden deri̇n öğrenme i̇le arazi̇ örtüsü hari̇tasi çikarilmasi