Past, present and prospect of an Artificial Intelligence (AI) based model for sediment transport prediction
An accurate model for sediment prediction is a priority for all hydrological researchers. Many
conventional methods have shown an inability to achieve an accurate prediction of …
conventional methods have shown an inability to achieve an accurate prediction of …
Multi-station artificial intelligence based ensemble modeling of reference evapotranspiration using pan evaporation measurements
In this study, different Artificial Intelligence (AI) techniques including Feed Forward Neural
Network (FFNN), Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector …
Network (FFNN), Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector …
Two decades on the artificial intelligence models advancement for modeling river sediment concentration: State-of-the-art
Simulation approaches employed in sediment processes are important for watershed
management and environmental impact assessment. Use of Stochastic models that based …
management and environmental impact assessment. Use of Stochastic models that based …
Application of artificial neural networks, support vector machine and multiple model-ANN to sediment yield prediction
Sediment yield is important for maintaining soil health, reservoir sustainability,
environmental pollution, and conservation of natural resources. The main aim of the present …
environmental pollution, and conservation of natural resources. The main aim of the present …
Using self-organizing maps and wavelet transforms for space–time pre-processing of satellite precipitation and runoff data in neural network based rainfall–runoff …
In this paper, a two-level self-organizing map (SOM) clustering technique was used to
identify spatially homogeneous clusters of precipitation satellite data, and to choose the …
identify spatially homogeneous clusters of precipitation satellite data, and to choose the …
Landslide susceptibility map** at Zonouz Plain, Iran using genetic programming and comparison with frequency ratio, logistic regression, and artificial neural …
Without a doubt, landslide is one of the most disastrous natural hazards and landslide
susceptibility maps (LSMs) in regional scale are the useful guide to future development …
susceptibility maps (LSMs) in regional scale are the useful guide to future development …
Multi-step ahead modeling of reference evapotranspiration using a multi-model approach
Abstract Efficient estimation of Reference Evapotranspiration (ET 0) becomes necessary for
water resources management and irrigation practices. Despite research advancement in the …
water resources management and irrigation practices. Despite research advancement in the …
Using Random Forest, a machine learning approach to predict nitrogen, phosphorus, and sediment event mean concentrations in urban runoff
Estimating pollutant loads from developed watersheds is vitally important to reduce nonpoint
source pollution from urban areas, as a key tool in meeting water quality goals is the …
source pollution from urban areas, as a key tool in meeting water quality goals is the …
Daily and monthly suspended sediment load predictions using wavelet based artificial intelligence approaches
In the current study, the efficiency of Wavelet-based Least Square Support Vector Machine
(WLSSVM) model was examined for prediction of daily and monthly Suspended Sediment …
(WLSSVM) model was examined for prediction of daily and monthly Suspended Sediment …
Suspended sediment modeling using neuro-fuzzy embedded fuzzy c-means clustering technique
The assessment of the suspended sediment (SS) amount in rivers has an importance
because it specifically affects the design and operation of numerous hydraulic structures …
because it specifically affects the design and operation of numerous hydraulic structures …