Performance comparison of an LSTM-based deep learning model versus conventional machine learning algorithms for streamflow forecasting

M Rahimzad, A Moghaddam Nia, H Zolfonoon… - Water Resources …, 2021 - Springer
Streamflow forecasting plays a key role in improvement of water resource allocation,
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

A Bardhan, P Samui, K Ghosh, AH Gandomi… - Applied Soft …, 2021 - Elsevier
This study proposes novel integration of extreme learning machine (ELM) and adaptive
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

N Kardani, A Bardhan, B Roy, P Samui… - Engineering with …, 2022 - Springer
Tight carbonate reservoirs appear to be heterogeneous due to the patchy production of
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 …

A Bardhan, N Kardani, AK Alzo'ubi, B Roy… - Journal of Rock …, 2022 - Elsevier
The study proposes an improved Harris hawks optimization (IHHO) algorithm by integrating
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 …

A comparative assessment of metaheuristic optimized extreme learning machine and deep neural network in multi-step-ahead long-term rainfall prediction for all …

R Kumar, MP Singh, B Roy, AH Shahid - Water Resources Management, 2021 - Springer
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 …

A new modeling approach for spatial prediction of flash flood with biogeography optimized CHAID tree ensemble and remote sensing data

VN Nguyen, P Yariyan, M Amiri, A Dang Tran… - Remote Sensing, 2020 - mdpi.com
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 …

Data-driven approach for rainfall-runoff modelling using equilibrium optimizer coupled extreme learning machine and deep neural network

B Roy, MP Singh, MR Kaloop, D Kumar, JW Hu… - Applied Sciences, 2021 - mdpi.com
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 …

Comparison of four bio-inspired algorithms to optimize KNEA for predicting monthly reference evapotranspiration in different climate zones of China

J Dong, X Liu, G Huang, J Fan, L Wu, J Wu - Computers and Electronics in …, 2021 - Elsevier
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 …

Surrogate models for the compressive strength mapping of cement mortar materials

PG Asteris, L Cavaleri, HB Ly, BT Pham - Soft Computing, 2021 - Springer
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 …