Machine learning in geo-and environmental sciences: From small to large scale
In recent years significant breakthroughs in exploring big data, recognition of complex
patterns, and predicting intricate variables have been made. One efficient way of analyzing …
patterns, and predicting intricate variables have been made. One efficient way of analyzing …
Recent trends on nanofluid heat transfer machine learning research applied to renewable energy
Nanofluids have received increasing attention in research and development in the area of
renewable and sustainable energy systems. The addition of a small amount of high thermal …
renewable and sustainable energy systems. The addition of a small amount of high thermal …
Machine learning for hydrologic sciences: An introductory overview
The hydrologic community has experienced a surge in interest in machine learning in recent
years. This interest is primarily driven by rapidly growing hydrologic data repositories, as …
years. This interest is primarily driven by rapidly growing hydrologic data repositories, as …
Soft computing ensemble models based on logistic regression for groundwater potential mapping
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 …
Novel hybrid intelligence models for flood-susceptibility prediction: Meta optimization of the GMDH and SVR models with the genetic algorithm and harmony search
Floods are among the deadliest natural hazards for humans and the environment.
Identifying the most flood-susceptible areas is a fundamental step in the development of …
Identifying the most flood-susceptible areas is a fundamental step in the development of …
Groundwater level modeling with machine learning: a systematic review and meta-analysis
Groundwater is a vital source of freshwater, supporting the livelihood of over two billion
people worldwide. The quantitative assessment of groundwater resources is critical for …
people worldwide. The quantitative assessment of groundwater resources is critical for …
Spatial prediction of landslide susceptibility using hybrid support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS) with various …
Landslides are natural and sometimes quasi-natural hazards that are destructive to natural
resources and cause loss of human life every year. Hence, preparing susceptibility maps for …
resources and cause loss of human life every year. Hence, preparing susceptibility maps for …
Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping
Using multiple ensemble learning techniques for improving the predictive accuracy of
landslide models is an active research area. In this study, we combined a radial basis …
landslide models is an active research area. In this study, we combined a radial basis …
[HTML][HTML] Modeling groundwater potential using novel GIS-based machine-learning ensemble techniques
Study region The present study has been carried out in the Tabriz River basin (5397 km 2) in
north-western Iran. Elevations vary from 1274 to 3678 m above sea level, and slope angles …
north-western Iran. Elevations vary from 1274 to 3678 m above sea level, and slope angles …
[HTML][HTML] Evaluating the predictive power of different machine learning algorithms for groundwater salinity prediction of multi-layer coastal aquifers in the Mekong Delta …
Groundwater salinization is considered as a major environmental problem in worldwide
coastal areas, influencing ecosystems and human health. However, an accurate prediction …
coastal areas, influencing ecosystems and human health. However, an accurate prediction …