Algorithms in low-code-no-code for research applications: a practical review
F Sufi - Algorithms, 2023 - mdpi.com
Algorithms have evolved from machine code to low-code-no-code (LCNC) in the past 20
years. Observing the growth of LCNC-based algorithm development, the CEO of GitHub …
years. Observing the growth of LCNC-based algorithm development, the CEO of GitHub …
Landslide identification using machine learning techniques: Review, motivation, and future prospects
Abstract The WHO (World Health Organization) study reports that, between 1998-2017, 4.8
million people have been affected by landslides with more than 18000 deaths. The …
million people have been affected by landslides with more than 18000 deaths. The …
[HTML][HTML] A comprehensive comparison among metaheuristics (MHs) for geohazard modeling using machine learning: Insights from a case study of landslide …
Abstract Machine learning (ML) has been extensively applied to model geohazards, yielding
tremendous success. However, researchers and practitioners still face challenges in …
tremendous success. However, researchers and practitioners still face challenges in …
A novel method using explainable artificial intelligence (XAI)-based Shapley Additive Explanations for spatial landslide prediction using Time-Series SAR dataset
As artificial intelligence (AI) techniques are becoming more popular in landslide modeling, it
is important to understand how decisions are made. Fairness, and transparency becomes …
is important to understand how decisions are made. Fairness, and transparency becomes …
Landslide susceptibility mapping using CNN-1D and 2D deep learning algorithms: comparison of their performance at Asir Region, KSA
To be proactive in mountain hazard mitigation, landslide disaster assessments are
becoming increasingly urgent. In this study, three modeling techniques, namely, support …
becoming increasingly urgent. In this study, three modeling techniques, namely, support …
Toward the reliable prediction of reservoir landslide displacement using earthworm optimization algorithm-optimized support vector regression (EOA-SVR)
Reliable prediction of reservoir displacement is essential for practical applications. Machine
learning offers an attractive and accessible set of tools for the displacement prediction of …
learning offers an attractive and accessible set of tools for the displacement prediction of …
Handling data imbalance in machine learning based landslide susceptibility mapping: a case study of Mandakini River Basin, North-Western Himalayas
Abstract Machine learning methods require a vast amount of data to train a model. The data
necessary for landslide susceptibility mapping is a collection of landslide causative factors …
necessary for landslide susceptibility mapping is a collection of landslide causative factors …
CAS Landslide Dataset: A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide Detection
In this work, we present the CAS Landslide Dataset, a large-scale and multisensor dataset
for deep learning-based landslide detection, developed by the Artificial Intelligence Group at …
for deep learning-based landslide detection, developed by the Artificial Intelligence Group at …
Exploring the uncertainty of landslide susceptibility assessment caused by the number of non–landslides
Q Liu, A Tang, D Huang - Catena, 2023 - Elsevier
Identifying the uncertainty caused by the number of non-landslides is necessary to obtain a
precise landslide susceptibility map. Hence, the objective of this study is to investigate the …
precise landslide susceptibility map. Hence, the objective of this study is to investigate the …
An integrated neural network method for landslide susceptibility assessment based on time-series InSAR deformation dynamic features
Y He, Z Zhao, Q Zhu, T Liu, Q Zhang… - … Journal of Digital …, 2024 - Taylor & Francis
We develop an integrated neural network landslide susceptibility assessment (LSA) method
that integrates temporal dynamic features of interferometry synthetic aperture radar (InSAR) …
that integrates temporal dynamic features of interferometry synthetic aperture radar (InSAR) …