Influence of data splitting on performance of machine learning models in prediction of shear strength of soil
The main objective of this study is to evaluate and compare the performance of different
machine learning (ML) algorithms, namely, Artificial Neural Network (ANN), Extreme …
machine learning (ML) algorithms, namely, Artificial Neural Network (ANN), Extreme …
[HTML][HTML] Flood susceptibility modelling using advanced ensemble machine learning models
Floods are one of nature's most destructive disasters because of the immense damage to
land, buildings, and human fatalities. It is difficult to forecast the areas that are vulnerable to …
land, buildings, and human fatalities. It is difficult to forecast the areas that are vulnerable to …
Flash flood susceptibility modeling using new approaches of hybrid and ensemble tree-based machine learning algorithms
Flash flooding is considered one of the most dynamic natural disasters for which measures
need to be taken to minimize economic damages, adverse effects, and consequences by …
need to be taken to minimize economic damages, adverse effects, and consequences by …
Identifying the essential conditioning factors of landslide susceptibility models under different grid resolutions using hybrid machine learning: A case of Wushan and …
This study attempts to identify the essential conditioning factors of landslides to increase the
predictive ability of landslide susceptibility models and explore the effects of different grid …
predictive ability of landslide susceptibility models and explore the effects of different grid …
Flood susceptibility map** of the Western Ghat coastal belt using multi-source geospatial data and analytical hierarchy process (AHP)
S Das - Remote Sensing Applications: Society and …, 2020 - Elsevier
Progressive environmental and climate change have altogether expanded hydro-
meteorological hazards everywhere throughout the world. In recent times, floods have …
meteorological hazards everywhere throughout the world. In recent times, floods have …
[HTML][HTML] Flash flood susceptibility map** using a novel deep learning model based on deep belief network, back propagation and genetic algorithm
Flash floods are responsible for loss of life and considerable property damage in many
countries. Flood susceptibility maps contribute to flood risk reduction in areas that are prone …
countries. Flood susceptibility maps contribute to flood risk reduction in areas that are prone …
Soft computing ensemble models based on logistic regression for groundwater potential map**
Groundwater potential maps are one of the most important tools for the management of
groundwater storage resources. In this study, we proposed four ensemble soft computing …
groundwater storage resources. In this study, we proposed four ensemble soft computing …
An effective geospatial-based flash flood susceptibility assessment with hydrogeomorphic responses on groundwater recharge
Floods are one of the most common natural disasters, resulting in the extensive destruction
of infrastructure, property, and human life. The destructive potential of a flood depends on …
of infrastructure, property, and human life. The destructive potential of a flood depends on …
Landslide susceptibility map** using machine learning algorithms and remote sensing data in a tropical environment
We used AdaBoost (AB), alternating decision tree (ADTree), and their combination as an
ensemble model (AB-ADTree) to spatially predict landslides in the Cameron Highlands …
ensemble model (AB-ADTree) to spatially predict landslides in the Cameron Highlands …
Ensemble models of GLM, FDA, MARS, and RF for flood and erosion susceptibility map**: a priority assessment of sub-basins
The mountainous watersheds are increasingly challenged with extreme erosions and
devastating floods due to climate change and human interventions. Hazard map** is …
devastating floods due to climate change and human interventions. Hazard map** is …