LEADER 00857cam0-22003251i-450- 001 990000341850403321 005 20061002111036.0 035 $a000034185 035 $aFED01000034185 035 $a(Aleph)000034185FED01 035 $a000034185 100 $a20020821d1885----km-y0itay50------ba 101 0 $afre 102 $aFR 105 $aac------001yy 200 1 $a<>origines de l'alchimie$fpar M. Berthelot 210 $aParis$cG. Steinheil$d1885 215 $aXX, 445 p.$cill., 1 ritr.$d23 cm 610 0 $aAlchimia$aStoria 676 $a540 676 $a540.112 700 1$aBerthelot,$bMarcellin$f<1827-1907> 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990000341850403321 952 $a04 223-9$bCI 393$fDINCH 952 $a13 AR 19 B 57$b2200$fFINBC 959 $aDINCH 959 $aFINBC 997 $aUNINA LEADER 05226nam 2200385 450 001 9910598030803321 005 20230328105301.0 035 $a(CKB)4100000003273630 035 $a(NjHacI)994100000003273630 035 $a(EXLCZ)994100000003273630 100 $a20230328d2018 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aApplication of artificial neural networks in geoinformatics /$fSaro Lee, editor 210 1$aBasel :$cMDPI AG - Multidisciplinary Digital Publishing Institute,$d[2018] 210 4$dİ2018 215 $a1 online resource (228 pages) $cillustrations 311 $a3-03842-742-X 327 $aAbout the Special Issue Editor v -- Saro Lee -- Editorial for Special Issue: Application of Artificial Neural Networks in Geoinformatics doi: 10.3390/app8010055 1 -- Sunmin Lee, Moung-Jin Lee and Hyung-Sup Jung Data Mining Approaches for Landslide Susceptibility Mapping in Umyeonsan, Seoul, South Korea doi: 10.3390/app7070683 4 Hyun-Joo Oh and Saro Lee -- Shallow Landslide Susceptibility Modeling Using the Data Mining Models Artificial Neural Network and Boosted Tree doi: 10.3390/app7101000 25 -- Saro Lee, Sunmin Lee, Wonkyong Song and Moung-Jin Lee -- Habitat Potential Mapping of Marten (Martes flavigula) and Leopard Cat (Prionailurus bengalensis) in South Korea Using Artificial Neural Network Machine Learning doi: 10.3390/app7090912 39 -- Syyed Adnan Raheel Shah, Tom Brijs, Naveed Ahmad, Ali Pirdavani, Yongjun Shen and Muhammad Aamir Basheer -- Road Safety Risk Evaluation Using GIS-Based Data Envelopment Analysis-Artificial Neural Networks Approach doi: 10.3390/app7090886 54 -- Mustafa Ridha Mezaal, Biswajeet Pradhan, Maher Ibrahim Sameen, Helmi Zulhaidi Mohd Shafri and Zainuddin Md Yusoff -- Optimized Neural Architecture for Automatic Landslide Detection from High-Resolution Airborne Laser Scanning Data doi: 10.3390/app7070730 73 -- Guandong Chen, Yu Li, Guangmin Sun and Yuanzhi Zhang -- Application of Deep Networks to Oil Spill Detection Using Polarimetric Synthetic Aperture Radar Images doi: 10.3390/app7100968 93 -- Jeong-In Hwang, Sung-Ho Chae, Daeseong Kim and Hyung-Sup Jung -- Application of Artificial Neural Networks to Ship Detection from X-Band Kompsat5 Imagery doi: 10.3390/app7090961 108 -- Alessandro Piscini, Vito Romaniello, Christian Bignami and Salvatore Stramondo A New Damage Assessment Method by Means of Neural Network and Multi-Sensor -- Satellite Data doi: 10.3390/app7080781 122 Books MDPI -- Prima Riza Kadavi, Won-Jin Lee and Chang-Wook Lee Analysis of the Pyroclastic Flow Deposits of Mount Sinabung and Merapi Using Landsat -- Imagery and the Artificial Neural Networks Approach doi: 10.3390/app7090935 132 -- Soo-Kyung Kwon, Hyung-Sup Jung, Won-Kyung Baek and Daeseong Kim -- Classification of Forest Vertical Structure in South Korea from Aerial Orthophoto and Lidar Data Using an Artificial Neural Network doi: 10.3390/app7101046 146 -- Giles M. Foody Impacts of Sample Design for Validation Data on the Accuracy of Feedforward Neural -- Network Classification doi: 10.3390/app7090888-- Young-Ji Byon, Jun Su Ha, Chung-Suk Cho, Tae-Yeon Kim and Chan Yeob Yeun -- Real-Time Transportation Mode Identification Using Artificial Neural Networks Enhanced with Mode Availability Layers: A Case Study in Dubai doi: 10.3390/app7090923 174 -- Maher Ibrahim Sameen and Biswajeet Pradhan -- Severity Prediction of Traffic Accidents with Recurrent Neural Networks doi: 10.3390/app7060476 191 -- N ´adia F. Afonso and Jos´e C. M. Pires -- Characterization of Surface Ozone Behavior at Different Regimes doi: 10.3390/app7090944 208. 330 $aRecently, a need has arisen for prediction techniques that can address a variety of problems by combining methods from the rapidly developing field of machine learning with geoinformation technologies such as GIS, remote sensing, and GPS. As a result, over the last few decades, one particular machine learning technology, known as artificial neural networks, has been successfully applied to a wide range of fields in science and engineering. In addition, the development of computational and spatial technologies has led to the rapid growth of geoinformatics, which specializes in the analysis of spatial information. Thus, recently, artificial neural networks have been applied to geoinformatics and have produced valuable results in the fields of geoscience, environment, natural hazards, natural resources, and engineering. Hence, this Special Issue of the journal Applied Sciences, "Application of Artificial Neural Networks in Geoinformatics," was successfully planned, and we here publish a collection of papers detailing novel contributions that are of relevance to these topics. 606 $aGeoinformatics 606 $aNeural networks (Computer science) 615 0$aGeoinformatics. 615 0$aNeural networks (Computer science) 676 $a550.285 702 $aLee$b Saro 801 0$bNjHacI 801 1$bNjHacl 906 $aBOOK 912 $a9910598030803321 996 $aApplication of Artificial Neural Networks in Geoinformatics$92948970 997 $aUNINA