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A combined deep CNN-RNN network for rainfall-runoff modelling in Bardha Water…
In recent years, there has been a growing interest in using artificial intelligence (AI) for rainfall-runoff modelling, as it has shown promising adaptability in this context. The current study involved the use of six distinct AI models to simulate monthly rainfall-runoff modelling in the Bardha watershed, India. These models included the artificial neural network (ANN), k-nearest neighbour reg…
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 13 hlm PDF, 6.872 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
A 3D convolutional neural network model with multiple outputs for simultaneou…
Porosity, tortuosity, specific surface area (SSA), and permeability are four key parameters of reactive transport modeling in sandstone, which are important for understanding solute transport and geochemical reaction processes in sandstone aquifers. These four parameters reflect the characteristics of pore structure of sandstone from different perspectives, and the traditional empirical formula…
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 10 hlm PDF, 54.556 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Flood susceptibility assessment using artificial neural networks in Indonesia
Flood incidents can massively damage and disrupt a city economic or governing core. However, flood risk can be mitigated through event planning and city-wide preparation to reduce damage. For, governments, firms, and civilians to make such preparations, flood susceptibility predictions are required. To predict flood susceptibility nine environmental related factors have been identified. They ar…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 8 hlm PDF, 1.503 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
A comparative analysis of super-resolution techniques for enhancing micro-CT …
High-resolution digital rock micro-CT images captured from a wide field of view are essential for various geosystem engineering and geoscience applications. However, the resolution of these images is often constrained by the capabilities of scanners. To overcome this limitation and achieve superior image quality, advanced deep learning techniques have been used. This study compares four differe…
- Edisi
- Vol.20, December 2023
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 17 hlm PDF, 6.377 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Deep learning techniques for hyperspectral image analysis in agriculture: A r…
In recent years, there has been a growing emphasis on assessing and ensuring the quality of horticultural and agricultural produce. Traditional methods involving field measurements, investigations, and statistical analyses are labour-intensive, time-consuming, and costly. As a solution, Hyperspectral Imaging (HSI) has emerged as a non-destructive and environmentally friendly technology. HSI has…
- Edisi
- Vol.12, April 2024
- ISBN/ISSN
- 1872-8235
- Deskripsi Fisik
- 19 hlm PDF, 3.154 KB
- Judul Seri
- ISPRS Open Journal of Photogrammetry and Remote Sensing
- No. Panggil
- 621.3678
Instance segmentation of standing dead trees in dense forest from aerial imag…
Mapping standing dead trees, especially, in natural forests is very important for evaluation of the forest's health status, and its capability for storing Carbon, and the conservation of biodiversity. Apparently, natural forests have larger areas which renders the classical field surveying method very challenging, time-consuming, labor-intensive, and unsustainable. Thus, for effective forest ma…
- Edisi
- Vol.6, December 2022
- ISBN/ISSN
- 1872-8235
- Deskripsi Fisik
- 14 hlm PDF, 17.476 KB
- Judul Seri
- ISPRS Open Journal of Photogrammetry and Remote Sensing
- No. Panggil
- -
Transfer learning from citizen science photographs enables plant species iden…
Accurate information on the spatial distribution of plant species and communities is in high demand for various fields of application, such as nature conservation, forestry, and agriculture. A series of studies has shown that Convolutional Neural Networks (CNNs) accurately predict plant species and communities in high-resolution remote sensing data, in particular with data at the centimeter sca…
- Edisi
- Vol.5, August 2022
- ISBN/ISSN
- 1872-8235
- Deskripsi Fisik
- 23 hlm PDF, 83.917 KB
- Judul Seri
- ISPRS Open Journal of Photogrammetry and Remote Sensing
- No. Panggil
- 621.3678
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