Comprehensive Water Quality Indicators Modeling by Environmental Protection View using New Optimized Weighted Ensemble Machine Learnings and Multi Algorithms DOI
Mojtaba Poursaeid

Process Safety and Environmental Protection, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 1, 2024

Language: Английский

Modeling of wetlands storage instabilities using an optimized slffn machine learning using evolutionary computation considering the RROC analysis and PDF techniques DOI
Mojtaba Poursaeid

Earth Science Informatics, Journal Year: 2024, Volume and Issue: 17(4), P. 3137 - 3148

Published: May 25, 2024

Language: Английский

Citations

7
Water quality fluctuations prediction and Debi estimation based on stochastic optimized weighted ensemble learning machine DOI
Mojtaba Poursaeid, Amir Hossein Poursaeed, Saeid Shabanlou

et al.

Process Safety and Environmental Protection, Journal Year: 2024, Volume and Issue: 188, P. 1160 - 1174

Published: June 2, 2024

Language: Английский

Citations

6
Predicting seawater intrusion in coastal areas using machine learning: A case study of arid coastal aquifers, Saudi Arabia DOI
Galal M. BinMakhashen, Mohammed Benaafi

Groundwater for Sustainable Development, Journal Year: 2024, Volume and Issue: 26, P. 101300 - 101300

Published: July 27, 2024

Language: Английский

Citations

5
Evaluation and machine learning prediction on thermal performance of energy walls in underground spaces as part of ground source heat pump systems DOI

Shuaijun Hu,

Gangqiang Kong,

Yinzhe Hong

et al.

Tunnelling and Underground Space Technology, Journal Year: 2024, Volume and Issue: 148, P. 105750 - 105750

Published: April 6, 2024

Language: Английский

Citations

4
Water potability classification based on hybrid stacked model and feature selection DOI
Ahmed M. Elshewey,

Rasha Y. Youssef,

Hazem M. El‐Bakry

et al.

Environmental Science and Pollution Research, Journal Year: 2025, Volume and Issue: unknown

Published: March 6, 2025

Language: Английский

Citations

0
Machine Learning Approaches for Assessing Groundwater Quality and Its Implications for Water Conservation in the Sub-tropical Capital Region of India DOI
Nand Lal Kushwaha,

Madhumita Sahoo,

Nilesh Biwalkar

et al.

Water Conservation Science and Engineering, Journal Year: 2025, Volume and Issue: 10(1)

Published: March 10, 2025

Language: Английский

Citations

0
Unraveling the water quality-ecosystem nexus using Kalman filter-driven models and feature analysis under uncertainty DOI
Mojtaba Poursaeid

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 133092 - 133092

Published: March 1, 2025

Language: Английский

Citations

0
Enhancing chlorophyll-a predictions using optimal machine learning models and field spectral reflectance DOI
Mona Allam,

Zhang Lifu,

Xuejian Sun

et al.

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(2)

Published: April 28, 2025

Language: Английский

Citations

0
An optimized Extreme Learning Machine by Evolutionary Computation for River Flow Prediction and Simulation of Water Pollution in Colorado River Basin, USA DOI
Mojtaba Poursaeid

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 233, P. 120998 - 120998

Published: July 16, 2023

Language: Английский

Citations

9
Maximum energy entropy: A novel signal preprocessing approach for data-driven monthly streamflow forecasting DOI Creative Commons
Alireza B. Dariane, Mohammad Reza M. Behbahani

Ecological Informatics, Journal Year: 2023, Volume and Issue: 79, P. 102452 - 102452

Published: Dec. 28, 2023

In recent years, the application of Data-Driven Models (DDMs) in ecological studies has garnered significant attention due to their capacity accurately simulate complex hydrological processes. These models have proven invaluable comprehending and predicting natural phenomena. However, achieve improved outcomes, certain additive components such as signal analysis (SAM) input variable selections (IVS) are necessary. SAMs unveil hidden characteristics within time series data, while IVS prevents utilization inappropriate data. realm research, understanding these patterns is pivotal for grasping implications streamflow dynamics guiding effective management decisions. Addressing need more precise forecasting, this study proposes a novel SAM called "Maximum Energy Entropy (MEE)" forecast monthly Ajichai basin, located northwestern Iran. A comparative was conducted, pitting MEE against well-known methods Discreet Wavelet (DW) Wavelet-Entropy (DWE), ultimately demonstrating superiority MEE. The results showcased superior performance our proposed method, with an NSE value 0.72, compared DW (NSE 0.68) DWE 0.68). Furthermore, exhibited greater reliability, boasting lower Standard Deviation 0.13 (0.26) (0.19). equips researchers decision-makers accurate predictions, facilitating well-informed water resource planning. To further evaluate MEE's accuracy using various DDMs, we integrated Artificial Neural Network (ANN) Genetic Programming (GP). Additionally, GP served method selecting appropriate variables. Ultimately, combination ANN forecasting model (MEE-GP-ANN) yielded most favorable results.

Language: Английский

Citations

8