[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 …
[HTML][HTML] Predicting flood susceptibility using LSTM neural networks
Identifying floods and producing flood susceptibility maps are crucial steps for decision-
makers to prevent and manage disasters. Plenty of studies have used machine learning …
makers to prevent and manage disasters. Plenty of studies have used machine learning …
GIS-based comparative assessment of flood susceptibility mapping using hybrid multi-criteria decision-making approach, naïve Bayes tree, bivariate statistics and …
Flood is a devastating natural hazard that may cause damage to the environment
infrastructure, and society. Hence, identifying the susceptible areas to flood is an important …
infrastructure, and society. Hence, identifying the susceptible areas to flood is an important …
Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India
This study is an attempt to quantitatively test and compare novel advanced-machine
learning algorithms in terms of their performance in achieving the goal of predicting flood …
learning algorithms in terms of their performance in achieving the goal of predicting flood …
Flood susceptibility modeling in Teesta River basin, Bangladesh using novel ensembles of bagging algorithms
The flooding in Bangladesh during monsoon season is very common and frequently
happens. Consequently, people have been experiencing tremendous damage to properties …
happens. Consequently, people have been experiencing tremendous damage to properties …
[HTML][HTML] Modeling fragmentation probability of land-use and land-cover using the bagging, random forest and random subspace in the Teesta River Basin …
S Talukdar, KU Eibek, S Akhter, SK Ziaul… - Ecological …, 2021 - Elsevier
Land-use and land-cover (LULC) changes have become a crucial issue that urgently needs
to be addressed due to global environmental change. Many studies have employed remote …
to be addressed due to global environmental change. Many studies have employed remote …
Improvement of best first decision trees using bagging and dagging ensembles for flood probability mapping
Abstract Development of zoning and flood-forecasting models is essential for making
optimal management decisions before and after floods. The Komijan watershed of Markazi …
optimal management decisions before and after floods. The Komijan watershed of Markazi …
[HTML][HTML] Flood susceptibility zonation using advanced ensemble machine learning models within Himalayan foreland basin
S Ghosh, S Saha, B Bera - Natural Hazards Research, 2022 - Elsevier
Floods are considered as one of nature's most destructive fluvio-hydrological extremes
because of the massive damage to agricultural land, roads and buildings and human …
because of the massive damage to agricultural land, roads and buildings and human …
Flash-flood hazard using deep learning based on H2O R package and fuzzy-multicriteria decision-making analysis
The present study was done in order to simulate the flash-flood susceptibility across the
Suha river basin in Romania using a number of 3 hybrid models and fuzzy-AHP multicriteria …
Suha river basin in Romania using a number of 3 hybrid models and fuzzy-AHP multicriteria …
Using machine learning models, remote sensing, and GIS to investigate the effects of changing climates and land uses on flood probability
The purpose of this study is to investigate the effects of climate and land use changes on
flood susceptibility areas in the Tajan watershed, Iran. To do this, land use changes over the …
flood susceptibility areas in the Tajan watershed, Iran. To do this, land use changes over the …