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

и другие.

Environmental Research Engineering and Management, Год журнала: 2023, Номер 79(3), С. 95 - 107

Опубликована: Окт. 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.

Язык: Английский

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

и другие.

Elsevier eBooks, Год журнала: 2024, Номер unknown, С. 1 - 74

Опубликована: Янв. 1, 2024

Процитировано

12
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

и другие.

Groundwater for Sustainable Development, Год журнала: 2024, Номер unknown, С. 101337 - 101337

Опубликована: Сен. 1, 2024

Язык: Английский

Процитировано

4
Predicting construction delay risks in Saudi Arabian projects: A comparative analysis of CatBoost, XGBoost, and LGBM DOI
Saleh Alsulamy

Expert Systems with Applications, Год журнала: 2024, Номер unknown, С. 126268 - 126268

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

4
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

и другие.

Deleted Journal, Год журнала: 2025, Номер 28(1)

Опубликована: Март 27, 2025

Язык: Английский

Процитировано

0
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

и другие.

Drones, Год журнала: 2023, Номер 7(11), С. 668 - 668

Опубликована: Ноя. 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

Язык: Английский

Процитировано

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

и другие.

Journal of the Russian Universities Radioelectronics, Год журнала: 2023, Номер 26(4), С. 56 - 69

Опубликована: Сен. 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

Язык: Английский

Процитировано

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

и другие.

Environmental Research Engineering and Management, Год журнала: 2023, Номер 79(3), С. 95 - 107

Опубликована: Окт. 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.

Язык: Английский

Процитировано

0