Performance comparison of an LSTM-based deep learning model versus conventional machine learning algorithms for streamflow forecasting
Streamflow forecasting plays a key role in improvement of water resource allocation,
management and planning, flood warning and forecasting, and mitigation of flood damages …
management and planning, flood warning and forecasting, and mitigation of flood damages …
ELM-based adaptive neuro swarm intelligence techniques for predicting the California bearing ratio of soils in soaked conditions
This study proposes novel integration of extreme learning machine (ELM) and adaptive
neuro swarm intelligence (ANSI) techniques for the determination of California bearing ratio …
neuro swarm intelligence (ANSI) techniques for the determination of California bearing ratio …
A novel improved Harris Hawks optimization algorithm coupled with ELM for predicting permeability of tight carbonates
Tight carbonate reservoirs appear to be heterogeneous due to the patchy production of
various digenetic properties. Consequently, the permeability calculation of tight rocks is …
various digenetic properties. Consequently, the permeability calculation of tight rocks is …
[HTML][HTML] Novel integration of extreme learning machine and improved Harris hawks optimization with particle swarm optimization-based mutation for predicting soil …
The study proposes an improved Harris hawks optimization (IHHO) algorithm by integrating
the standard Harris hawks optimization (HHO) algorithm and mutation-based search …
the standard Harris hawks optimization (HHO) algorithm and mutation-based search …
Performance improvement of LSTM-based deep learning model for streamflow forecasting using Kalman filtering
F Bakhshi Ostadkalayeh, S Moradi, A Asadi… - Water Resources …, 2023 - Springer
Prediction of streamflow as a crucial source of hydrological information plays a central role
in various fields of water resources projects. While accurate daily streamflow forecasts are …
in various fields of water resources projects. While accurate daily streamflow forecasts are …
A comparative assessment of metaheuristic optimized extreme learning machine and deep neural network in multi-step-ahead long-term rainfall prediction for all …
Prediction of long-term rainfall patterns is a highly challenging task in the hydrological field
due to random nature of rainfall events. The contribution of monthly rainfall is important in …
due to random nature of rainfall events. The contribution of monthly rainfall is important in …
A new modeling approach for spatial prediction of flash flood with biogeography optimized CHAID tree ensemble and remote sensing data
Flash floods induced by torrential rainfalls are considered one of the most dangerous natural
hazards, due to their sudden occurrence and high magnitudes, which may cause huge …
hazards, due to their sudden occurrence and high magnitudes, which may cause huge …
Data-driven approach for rainfall-runoff modelling using equilibrium optimizer coupled extreme learning machine and deep neural network
Rainfall-runoff (RR) modelling is used to study the runoff generation of a catchment. The
quantity or rate of change measure of the hydrological variable, called runoff, is important for …
quantity or rate of change measure of the hydrological variable, called runoff, is important for …
Comparison of four bio-inspired algorithms to optimize KNEA for predicting monthly reference evapotranspiration in different climate zones of China
Accurate estimation of reference crop evapotranspiration (ET 0) is of great significance to
crop water use and agricultural water resources management. This study evaluated the …
crop water use and agricultural water resources management. This study evaluated the …
Surrogate models for the compressive strength mapping of cement mortar materials
Despite the extensive use of mortar materials in constructions over the last decades, there is
not yet a robust quantitative method available in the literature, which can reliably predict …
not yet a robust quantitative method available in the literature, which can reliably predict …