Improving streamflow prediction using a new hybrid ELM model combined with hybrid particle swarm optimization and grey wolf optimization
Accurate runoff estimation is crucial for optimal reservoir operation and irrigation purposes.
In this study, a novel hybrid method is proposed for monthly runoff prediction in Mangla …
In this study, a novel hybrid method is proposed for monthly runoff prediction in Mangla …
Coupling a hybrid CNN-LSTM deep learning model with a boundary corrected maximal overlap discrete wavelet transform for multiscale lake water level forecasting
Develo** accurate lake water level (WL) forecasting models is important for flood control,
shoreline maintenance and sustainable water resources planning and management. In this …
shoreline maintenance and sustainable water resources planning and management. In this …
Deep learning hybrid model with Boruta-Random forest optimiser algorithm for streamflow forecasting with climate mode indices, rainfall, and periodicity
Long-term forecasting of any hydrologic phenomena is essential for strategic environmental
planning, hydrologic and other forms of structural design, agriculture, and water resources …
planning, hydrologic and other forms of structural design, agriculture, and water resources …
Artificial intelligence modelling integrated with Singular Spectral analysis and Seasonal-Trend decomposition using Loess approaches for streamflow predictions
The nature of streamflow in the basins is stochastic and complex making it difficult to make
an accurate prediction about the future river flows. Recently, artificial neural-based deep …
an accurate prediction about the future river flows. Recently, artificial neural-based deep …
Prediction of seven-day compressive strength of field concrete
This study has explored nine machine learning methods that cover linear, non-linear, and
ensemble learning models to predict the compressive strength of field concrete at 7 days …
ensemble learning models to predict the compressive strength of field concrete at 7 days …
Application of Boruta algorithms as a robust methodology for performance evaluation of CMIP6 general circulation models for hydro-climatic studies
Regional climate models are essential for climate change projections and hydrologic
modelling studies, especially in watersheds that are overly sensitive to changes in climate …
modelling studies, especially in watersheds that are overly sensitive to changes in climate …
Weighting approaches in data mining and knowledge discovery: A review
Modeling and forecasting are impressive and active research areas, which have been
widely used in diverse theoretical and practical applications, successfully. Accuracy is the …
widely used in diverse theoretical and practical applications, successfully. Accuracy is the …
New double decomposition deep learning methods for river water level forecasting
Forecasting river water levels or streamflow water levels (SWL) is vital to optimising the
practical and sustainable use of available water resources. We propose a new deep …
practical and sustainable use of available water resources. We propose a new deep …
Soil moisture simulation using hybrid artificial intelligent model: Hybridization of adaptive neuro fuzzy inference system with grey wolf optimizer algorithm
Accurate estimation of soil moisture content is necessary for optimal management of water
and soil resources. Soil moisture is an important variable in the hydrologic cycle, which …
and soil resources. Soil moisture is an important variable in the hydrologic cycle, which …
UAV-based hyperspectral and ensemble machine learning for predicting yield in winter wheat
Z Li, Z Chen, Q Cheng, F Duan, R Sui, X Huang, H Xu - Agronomy, 2022 - mdpi.com
Winter wheat is a widely-grown cereal crop worldwide. Using growth-stage information to
estimate winter wheat yields in a timely manner is essential for accurate crop management …
estimate winter wheat yields in a timely manner is essential for accurate crop management …