| 1 |
Predicting sustainable arsenic mitigation using machine learning techniques
|
ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY
|
| 2 |
Swarm intelligence optimization of the group method of data handling using the cuckoo search and whale optimization algorithms to model and predict landslides
|
APPLIED SOFT COMPUTING
|
| 3 |
Hybridized neural fuzzy ensembles for dust source modeling and prediction
|
ATMOSPHERIC ENVIRONMENT
|
| 4 |
SWPT: An automated GIS-based tool for prioritization of sub-watersheds based on morphometric and topo-hydrological factors
|
Geoscience Frontiers
|
| 5 |
A Hybrid Computational Intelligence Approach to Groundwater Spring Potential Mapping
|
Water
|
| 6 |
A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran)
|
SENSORS
|
| 7 |
Modeling flood susceptibility using data-driven approaches of naive Bayes tree, alternating decision tree, and random forest methods
|
SCIENCE OF THE TOTAL ENVIRONMENT
|
| 8 |
Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model
|
Remote Sensing
|
| 9 |
Novel Hybrid Integration Approach of Bagging-Based Fisher’s Linear Discriminant Function for Groundwater Potential Analysis
|
Natural Resources Research
|
| 10 |
A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods
|
JOURNAL OF HYDROLOGY
|
| 11 |
Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm
|
Remote Sensing
|
| 12 |
Multi-Criteria Decision Making (MCDM) Model for Seismic Vulnerability Assessment (SVA) of Urban Residential Buildings
|
ISPRS International Journal of Geo-Information
|
| 13 |
Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping
|
SENSORS
|
| 14 |
Groundwater spring potential modelling: Comprising the capability and robustness of three different modeling approaches
|
JOURNAL OF HYDROLOGY
|
| 15 |
Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms
|
SENSORS
|
| 16 |
A novel hybrid integration model using support vector machines and random subspace for weather-triggered landslide susceptibility assessment in the Wuning area (China)
|
Environmental Earth Sciences
|
| 17 |
Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China
|
SCIENCE OF THE TOTAL ENVIRONMENT
|