Artificial intelligence models for suspended river sediment prediction: state-of-the art, modeling framework appraisal, and proposed future research directions
River sedimentation is an important indicator for ecological and geomorphological
assessments of soil erosion within any watershed region. Sediment transport in a river basin …
assessments of soil erosion within any watershed region. Sediment transport in a river basin …
[HTML][HTML] Application of remote sensing and machine learning algorithms for forest fire map** in a Mediterranean area
Forest fire disaster is currently the subject of intense research worldwide. The development
of accurate strategies to prevent potential impacts and minimize the occurrence of disastrous …
of accurate strategies to prevent potential impacts and minimize the occurrence of disastrous …
Evaluation of CMIP6 GCM rainfall in mainland Southeast Asia
Global climate models (GCMs) of Coupled Model Intercomparison Project 6 (CMIP6) has
designed with new socioeconomic pathway scenarios to incorporate the socioeconomic …
designed with new socioeconomic pathway scenarios to incorporate the socioeconomic …
A new application of deep neural network (LSTM) and RUSLE models in soil erosion prediction
Rainfall variation causes frequent unexpected disasters all over the world. Increasing rainfall
intensity significantly escalates soil erosion and soil erosion related hazards. Forecasting …
intensity significantly escalates soil erosion and soil erosion related hazards. Forecasting …
A research landscape bibliometric analysis on climate change for last decades: Evidence from applications of machine learning
Climate change (CC) is one of the greatest threats to human health, safety, and the
environment. Given its current and future impacts, numerous studies have employed …
environment. Given its current and future impacts, numerous studies have employed …
A stacking ensemble learning model for monthly rainfall prediction in the Taihu Basin, China
The prediction of monthly rainfall is greatly beneficial for water resources management and
flood control projects. Machine learning (ML) techniques, as an increasingly popular …
flood control projects. Machine learning (ML) techniques, as an increasingly popular …
Changes in reference evapotranspiration and its driving factors in peninsular Malaysia
Trends in reference evapotranspiration (ETo) have been found highly diverse in different
regions of the globe due to the contradictory changes in the meteorological variables that …
regions of the globe due to the contradictory changes in the meteorological variables that …
Integration of extreme gradient boosting feature selection approach with machine learning models: application of weather relative humidity prediction
Relative humidity (RH) is one of the important processes in the hydrology cycle which is
highly stochastic. Accurate RH prediction can be highly beneficial for several water …
highly stochastic. Accurate RH prediction can be highly beneficial for several water …
A comparison of machine learning models for predicting rainfall in urban metropolitan cities
Current research studies offer an investigation of machine learning methods used for
forecasting rainfall in urban metropolitan cities. Time series data, distinguished by their …
forecasting rainfall in urban metropolitan cities. Time series data, distinguished by their …
Boosted artificial intelligence model using improved alpha-guided grey wolf optimizer for groundwater level prediction: Comparative study and insight for federated …
Modeling groundwater level (GWL) is a challenging task particularly in intensive
groundwater-based irrigated regions due to its dependency on multiple natural and …
groundwater-based irrigated regions due to its dependency on multiple natural and …