Flood prediction using machine learning models: Literature review
Floods are among the most destructive natural disasters, which are highly complex to model.
The research on the advancement of flood prediction models contributed to risk reduction …
The research on the advancement of flood prediction models contributed to risk reduction …
Flood susceptibility map** with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory
Floods are one of the most widespread natural hazards occurring across the globe. The
main objective of this study was to produce flood susceptibility maps for the province of …
main objective of this study was to produce flood susceptibility maps for the province of …
[HTML][HTML] Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors
To perform landslide susceptibility prediction (LSP), it is important to select appropriate
map** unit and landslide-related conditioning factors. The efficient and automatic multi …
map** unit and landslide-related conditioning factors. The efficient and automatic multi …
Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection
There is a growing demand for detailed and accurate landslide maps and inventories
around the globe, but particularly in hazard-prone regions such as the Himalayas. Most …
around the globe, but particularly in hazard-prone regions such as the Himalayas. Most …
Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment
The main objective of the current study was to introduce a Deep Learning Neural Network
(DLNN) model in landslide susceptibility assessments and compare its predictive …
(DLNN) model in landslide susceptibility assessments and compare its predictive …
[HTML][HTML] A hybrid ensemble-based deep-learning framework for landslide susceptibility map**
Landslides are highly hazardous geological disasters that can potentially threaten the safety
of human life and property. As a result, landslide susceptibility map** (LSM) plays an …
of human life and property. As a result, landslide susceptibility map** (LSM) plays an …
Landslide susceptibility prediction based on a semi-supervised multiple-layer perceptron model
Conventional supervised and unsupervised machine learning models used for landslide
susceptibility prediction (LSP) have many drawbacks, such as an insufficient number of …
susceptibility prediction (LSP) have many drawbacks, such as an insufficient number of …
GIS-based landslide susceptibility assessment using optimized hybrid machine learning methods
X Chen, W Chen - Catena, 2021 - Elsevier
Globally, but especially in China, landslides are considered to be one of the most severe
and significant natural hazards. In this study, bivariate statistical-based kernel logistic …
and significant natural hazards. In this study, bivariate statistical-based kernel logistic …
[HTML][HTML] Landslide susceptibility map** using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia
AM Youssef, HR Pourghasemi - Geoscience Frontiers, 2021 - Elsevier
The current study aimed at evaluating the capabilities of seven advanced machine learning
techniques (MLTs), including, Support Vector Machine (SVM), Random Forest (RF) …
techniques (MLTs), including, Support Vector Machine (SVM), Random Forest (RF) …
Advances in computational intelligence of polymer composite materials: machine learning assisted modeling, analysis and design
The superior multi-functional properties of polymer composites have made them an ideal
choice for aerospace, automobile, marine, civil, and many other technologically demanding …
choice for aerospace, automobile, marine, civil, and many other technologically demanding …