Landslide Movement of Bendungan District Trenggalek Using an Artificial Neural Network DOI Open Access
Didik Taryana, Rudi Hartono, Dicky Arinta

et al.

Environmental Research Engineering and Management, Journal Year: 2023, Volume and Issue: 79(3), P. 95 - 107

Published: Oct. 13, 2023

Landslide is one of the disasters that often occurs in Indonesia East Java Province, especially Bendungan District, Trenggalek Regency. Analysis landslide susceptibility District needed to spatially locate occurrences. The purpose this study was predict events using an artificial neural network. Rainfall, topography, physical soil properties, and land-use were used as explanatory variables. An analytic hierarchy process approach applied determine weight model satisfactorily classified hazards with area under curve 0.96. northwest found be a region at high risk rainfall texture most influential parts triggering landslides.

Language: Английский

Artificial intelligence in civil engineering DOI
Nishant Raj Kapoor, Ashok Kumar, Anuj Kumar

et al.

Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 74

Published: Jan. 1, 2024

Citations

12
Wind farm sites selection using a machine learning approach and geographical information systems in Türkiye DOI Creative Commons

Oras Fadhil Khalaf,

Osman N. Uçan,

Naseem Adnan Alsamarai

et al.

Deleted Journal, Journal Year: 2025, Volume and Issue: 28(1)

Published: March 27, 2025

Language: Английский

Citations

0
Predicting construction delay risks in Saudi Arabian projects: A comparative analysis of CatBoost, XGBoost, and LGBM DOI
Saleh Alsulamy

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: unknown, P. 126268 - 126268

Published: Dec. 1, 2024

Language: Английский

Citations

3
Groundwater potential mapping in arid and semi-arid regions of Kurdistan region of Iraq: A geoinformatics-based machine learning approach DOI
Kaiwan K. Fatah, Yaseen T. Mustafa,

Imaddadin O. Hassan

et al.

Groundwater for Sustainable Development, Journal Year: 2024, Volume and Issue: unknown, P. 101337 - 101337

Published: Sept. 1, 2024

Language: Английский

Citations

2
Using UAVs and Machine Learning for Nothofagus alessandrii Species Identification in Mediterranean Forests DOI Creative Commons
Antonio Cabrera,

Miguel Peralta-Aguilera,

Paula V. Henríquez-Hernández

et al.

Drones, Journal Year: 2023, Volume and Issue: 7(11), P. 668 - 668

Published: Nov. 9, 2023

This study explores the use of unmanned aerial vehicles (UAVs) and machine learning algorithms for identification Nothofagus alessandrii (ruil) species in Mediterranean forests Chile. The endangered nature this species, coupled with habitat loss environmental stressors, necessitates efficient monitoring conservation efforts. UAVs equipped high-resolution sensors capture orthophotos, enabling development classification models using supervised techniques. Three algorithms—Random Forest (RF), Support Vector Machine (SVM), Maximum Likelihood (ML)—are evaluated, both at Pixel- Object-Based levels, across three areas. results reveal that RF consistently demonstrates strong performance, followed by SVM ML. choice algorithm training approach significantly impacts outcomes, highlighting importance tailored selection based on project requirements. These findings contribute to enhancing accuracy remote sensing applications, supporting biodiversity ecological research

Language: Английский

Citations

1
Development of an Environmental Monitoring System Based on Spatial Marking and Machine Vision Technologies DOI Creative Commons
Mark Zaslavskiy, K. E. Kryzhanovskiy, D. V. Ivanov

et al.

Journal of the Russian Universities Radioelectronics, Journal Year: 2023, Volume and Issue: 26(4), P. 56 - 69

Published: Sept. 29, 2023

Introduction. The use of available satellite images and aerial photography by unmanned vehicles (UAVs) in the tasks environmental monitoring is challenged imperfection existing tools. Geographic information systems are characterized insufficient flexibility to automatically work with heterogeneous sources. latest models based on artificial intelligence ecology require preliminary data preparation. article presents results designing a software system for machine vision sensor data, which provides unification while being flexible both terms sources methods their analysis. Aim . Creation generalized coordinated spatial marking tasks. Materials Software engineering methods, database theory markup image processing methods. Results A method unifying was developed. analysis open from remote sensing Earth, as well UAV approaches monitoring. To implement method, architecture designed, model document-oriented DBMS developed, allows storing scaling procedure. Conclusion tools were analyzed. an architecture, created. successfully implemented web interface

Language: Английский

Citations

0
Landslide Movement of Bendungan District Trenggalek Using an Artificial Neural Network DOI Open Access
Didik Taryana, Rudi Hartono, Dicky Arinta

et al.

Environmental Research Engineering and Management, Journal Year: 2023, Volume and Issue: 79(3), P. 95 - 107

Published: Oct. 13, 2023

Landslide is one of the disasters that often occurs in Indonesia East Java Province, especially Bendungan District, Trenggalek Regency. Analysis landslide susceptibility District needed to spatially locate occurrences. The purpose this study was predict events using an artificial neural network. Rainfall, topography, physical soil properties, and land-use were used as explanatory variables. An analytic hierarchy process approach applied determine weight model satisfactorily classified hazards with area under curve 0.96. northwest found be a region at high risk rainfall texture most influential parts triggering landslides.

Language: Английский

Citations

0