Learning GIS Using Open Source Software

Edisi

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ISBN/ISSN

9780367522124

Deskripsi Fisik

xiv, 225 hlm.; 25 cm.

Judul Seri

-

No. Panggil

910.285 SAH l

Geofisika Global dalam Survei Pengolahan dan Evaluasi Data Seismik, GPS serta…

Edisi

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ISBN/ISSN

9786232974586

Deskripsi Fisik

xviii, 90 hlm; 25 cm

Judul Seri

-

No. Panggil

551.46 NAN g

Integrating the artificial intelligence and hybrid machine learning algorithm…

The aim of the current work is to compare susceptibility maps of landslides produced using machine learning techniques i.e. multilayer perception neural nets (MLP), kernel logistic regression (KLR), random forest (RF), and multivariate adaptive regression splines (MARS); novel ensemble approaches i.e. MLP-Bagging, KLR-Bagging, RF-Bagging and MARS-Bagging in the Kurseong-Himalayan region. For th…

Ensemble hybrid machine learning methods for gully erosion susceptibility map…

Gully erosion is one of the important problems creating barrier to agricultural development. The present research used the radial basis function neural network (RBFnn) and its ensemble with random sub-space (RSS) and rotation forest (RTF) ensemble Meta classifiers for the spatial mapping of gully erosion susceptibility (GES) in Hinglo river basin. 120 gullies were marked and grouped into four-f…

Site suitability for Aromatic Rice cultivation by integrating Geo-spatial and…

The purpose of this work is to assess the soil fertility for Tulaipanji rice cultivation in Kaliyaganj C.D. Block using the Analytic Hierarchy Process (AHP) and Machine learning algorithms along with the field survey data and GIS. A total of 40 soil samples from Tulaipanji rice fields (from 0 to 40 ​cm depth) have been randomly collected for the analysis of the soil health condition. For the …

Deep learning for real-time P-wave detection: A case study in Indonesia’s e…

Detecting seismic events in real-time for prompt alerts and responses is a challenging task that requires accurately capturing P-wave arrivals. This task becomes even more challenging in regions like Indonesia, where widely spaced seismic stations exist. The wide station spacing makes associating the seismic signals with specific even more difficult. This paper proposes a novel deep learning-ba…

A new approach to dust source mapping using visual interpretation and object-…

The emission of dust particles, mainly from arid and semi-arid lands, as a result of climate change and human activities, is known to be a global issue. Identifying dust emission sources is the first key step in dealing with the hazardous consequences of this rising phenomenon. This study is an attempt to address one of the major challenges in mapping dust emission sources. Accordingly, an inno…

Low-cost real-time aerial object detection and GPS location tracking pipeline

Real-time object detection and tracking is an active area of aerial remote sensing research that enables many environmental and ecological monitoring and preservation applications. Despite the development of several solutions tailored for these specific applications, trade-offs between cost efficiency and feature richness persist. This paper proposes a lightweight, low-cost, and modular approac…

Deep unsupervised learning for 3D ALS point clouds change detection

Change detection from traditional 2D optical images has limited capability to model the changes in the height or shape of objects. Change detection using 3D point cloud from photogrammetry or LiDAR surveying can fill this gap by providing critical depth information. While most existing machine learning based 3D point cloud change detection methods are supervised, they severely depend on the ava…

Synergy for the sustainable of National development: Annual report 2017

Edisi

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ISBN/ISSN

-

Deskripsi Fisik

173 hlm. ; 29 cm.

Judul Seri

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No. Panggil

338.7 KEM s