Geotechnical Test and Effective Processing Analysis of Geotechnical Engineering Investigation Based on Geographic Information System Under BP Neural Network

2021 ◽  
pp. 269-275
Author(s):  
Shihai Wang
Keyword(s):  
Neural Network ◽  
Information System ◽  
Geographic Information System ◽  
Bp Neural Network ◽  
Geotechnical Engineering ◽  
Geographic Information ◽  
Engineering Investigation

scholarly journals Artificial Neural Network Model in Spatial Analysis of Geographic Information System

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Shaofu He ◽  
Fei Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Information System ◽  
Geographic Information System ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Geographic Information ◽  
Analysis Method ◽  
Artificial Neural

In the past two decades, the computer technology industry has developed rapidly, and the geological prospecting industry is also undergoing a computerized and electronic revolution. The application technology of new geological information systems is gradually adding us to the spatial information system of geological prospecting projects. In order to deeply study the current situation of the artificial neural network model in the spatial analysis of our country’s geographic information system, this paper uses the traditional classification analysis method; database analysis and neural network analysis method of compensating samples were collected, an artificial model of the network is established, and the algorithm is simplified. And a neural network model is created. In the research of A and B counties’ geographic information system, using a new network model, 61 geological disasters were found in County A, of which 47 were landslides, 4 collapses, and 10 unstable slopes. There were 19 geographical disasters in County B, including 9 unstable slopes, 6 landslides and 4 collapses. In terms of geographic prediction combined with the network model, the comparison with the actual situation shows that the geographical distribution is 99.7% in the geographical and geological disaster-prone areas, and the geographical distribution is less in the nonprone areas, with a proportion of 0.3%. Geological disaster-prone areas of low points accounted for 76.9%, and the number of disaster-affected points in the low-prone areas accounted for 22.8%. The geographical and geological grades divided by the evaluation model are basically consistent with the actual grades, which can meet the needs of geographic evaluation. It is basically realized that starting from the model’s geographic information system, a more comprehensive and practical artificial neural network model is designed.

Regional forest-fire susceptibility analysis in central Portugal using a probabilistic ratings procedure and artificial neural network weights assignment

2011 ◽  
Vol 20 (6) ◽  
pp. 776 ◽  
Author(s):  
Luca Antonio Dimuccio ◽  
Rui Ferreira ◽  
Lúcio Cunha ◽  
António Campar de Almeida
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Information System ◽  
Geographic Information System ◽  
Forest Fire ◽  
Geographic Information ◽  
Related Factors ◽  
Susceptibility Index ◽  
Artificial Neural ◽  
Burnt Areas

Geographic information system analysis and artificial neural network modelling were combined to evaluate forest-fire susceptibility in the Central Portugal administrative area. Data on forest fire events, indicated by burnt areas during the years from 1990 to 2007, were identified from official records. Topographic, supporting infrastructures, vegetation cover, climatic, demographic and satellite-image data were collected, processed and integrated into a spatial database using geographic information system techniques. Eight fire-related factors were extracted from the collected data, including topographic slope and aspect, road density, viewsheds from fire watchtowers, land cover, Landsat Normalised Difference Vegetation Index, precipitation and population density. Ratings were calculated for the classes or categories of each factor using a frequency-probabilistic procedure. The thematic layers (burnt areas and fire-related factors) were analysed using an advanced artificial neural network model to calculate the relative weight of each factor in explaining the distribution of burnt areas. A forest-fire susceptibility index was calculated using the trained back-propagation artificial neural network weights and the frequency-probabilistic ratings, and then a general forest-fire susceptibility index map was constructed in geographic information system. Burnt areas were used to evaluate the forest-fire susceptibility index map, and the results showed an agreement of 78%. This forest-fire susceptibility map can be used in strategic and operational forest-fire management planning at the regional scale.

scholarly journals Pemetaan Banjir Rob Medan Utara Menggunakan Artificial Neural Network dan Gis untuk Langkah Mitigasi

2021 ◽  
Vol 2 (8) ◽  
pp. 1512-1526
Author(s):  
Monang Panjaitan ◽  
Ahmad Perwira Mulia ◽  
Zaid Perdana Nasution
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Artificial Neural Network ◽  
Information System ◽  
Geographic Information System ◽  
Drainage Density ◽  
Geographic Information ◽  
Artificial Neural

Kota besar di Indonesia, seperti Jakarta, Surabaya, Semarang dan Medan terancam terhadap banjir rob, khususnya di wilayah utara yang berbatasan langsung dengan perairan laut. Ratusan warga di kawasan utara Medan mengalami banjir rob akibat pasang air laut yang merendam permukiman mereka. Perlunya memetakan zona terancam banjir rob berdasarkan faktor-faktor penyebab banjir rob di wilayah Medan Utara sebagai dasar bagi pemangku kepentingan terkait dalam rangka penanganan untuk mengurangi kerugian akibat banjir rob. Faktor kerawanan terhadap banjir rob mencakup data curah hujan, drainage density, land use (tata guna lahan), jarak ke sungai, jenis tanah, elevasi, kemiringan, aspek, jarak ke muara. Analisis kuantitatif terhadap data dilakukan menggunakan Geographic Information System (GIS) dan Artificial Neural Network. Lokasi penelitian adalah Kecamatan Medan Belawan, Marelan dan Medan Labuhan Kota Medan. Hasil penelitian dengan rumus MAPE menunjukkan akurasi data train percobaan 1 sebesar 64,54137% dan data tes percobaan 1 sebesar 71,0257%. Sementara data train percobaan 2 sebesar 71,0257% dan data tes percobaan 2 sebesar 45,67167%. Akurasi data train percobaan 2 menggunakan rumus nilai eror < 1,5 sebesar  92% dan data tes percobaan 2 sebesar 68,61%.

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