Cited by Enhancing Tree-Based Machine Learning for Chlorophyll-a Prediction in Coastal Seawater Through Spatiotemporal Feature Integration

Monthly streamflow forecasting by machine learning methods using dynamic weather prediction model outputs over Iran DOI

Mohammad Akbarian, Bahram Saghafian, Saeed Golian

et al.

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 620, P. 129480 - 129480

Published: April 12, 2023

Language: Английский

Citations

A novel agricultural drought index based on multi-source remote sensing data and interpretable machine learning DOI

Hao Chen, Yang Ni, Xuanhua Song

et al.

Agricultural Water Management, Journal Year: 2025, Volume and Issue: 308, P. 109303 - 109303

Published: Jan. 16, 2025

Language: Английский

Citations

Hybridization of Stochastic Hydrological Models and Machine Learning Methods for Improving Rainfall-Runoff Modelling DOI

Sianou Ezéckiel Houénafa,

Olatunji Johnson,

Erick Kiplangat Ronoh

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104079 - 104079

Published: Jan. 1, 2025

Language: Английский

Citations

Transfer Learning Framework for the Wind Pressure Prediction of High-Rise Building Surfaces Using Wind Tunnel Experiments and Machine Learning DOI

Jingyu Wei,

Tzung-Sz Shen,

Kun Wang

et al.

Building and Environment, Journal Year: 2025, Volume and Issue: unknown, P. 112620 - 112620

Published: Jan. 1, 2025

Language: Английский

Citations

Optimization algorithms for modeling conversion and naphtha yield in the catalytic co-cracking of plastic in HVGO DOI

A.G. Usman,

Abdullah Aitani, Jamilu Usman

et al.

Process Safety and Environmental Protection, Journal Year: 2025, Volume and Issue: unknown, P. 106958 - 106958

Published: Feb. 1, 2025

Language: Английский

Citations

Enhanced machine learning models development for flash flood mapping using geospatial data DOI

Yacine Hasnaoui, Salah Eddine Tachi, Hamza Bouguerra

et al.

Euro-Mediterranean Journal for Environmental Integration, Journal Year: 2024, Volume and Issue: 9(3), P. 1087 - 1107

Published: May 31, 2024

Language: Английский

Citations

Streamflow Estimation in a Mediterranean Watershed Using Neural Network Models: A Detailed Description of the Implementation and Optimization DOI

Ana R. Oliveira, Tiago B. Ramos, Ramiro Neves

et al.

Water, Journal Year: 2023, Volume and Issue: 15(5), P. 947 - 947

Published: March 1, 2023

This study compares the performance of three different neural network models to estimate daily streamflow in a watershed under natural flow regime. Based on existing and public tools, types NN were developed, namely, multi-layer perceptron, long short-term memory, convolutional network. Precipitation was either considered an input variable its own or combined with air temperature as another variable. Different periods accumulation, average, and/or delay considered. The models’ structures optimized automatically showed that CNN performed best, reaching, for example, Nash–Sutcliffe efficiency 0.86 root mean square error 4.2 m3 s−1. solution considers 1D layer dense output layers, respectively. Between those two layers are As variables, best reached when accumulated precipitation values 1 5, 10 days delayed by 7 days.

Language: Английский

Citations

An evolutionary hybrid method based on particle swarm optimization algorithm and extreme gradient boosting for short-term streamflow forecasting DOI

Hüseyin Çağan Kılınç, Bülent Haznedar, Furkan Ozkan

et al.

Acta Geophysica, Journal Year: 2024, Volume and Issue: 72(5), P. 3661 - 3681

Published: Feb. 25, 2024

Language: Английский

Citations

Drinking Water Resources Suitability Assessment Based on Pollution Index of Groundwater Using Improved Explainable Artificial Intelligence DOI

Sani I. Abba, Mohamed A. Yassin, Auwalu Saleh Mubarak

et al.

Sustainability, Journal Year: 2023, Volume and Issue: 15(21), P. 15655 - 15655

Published: Nov. 6, 2023

The global significance of fluoride and nitrate contamination in coastal areas cannot be overstated, as these contaminants pose critical environmental public health challenges across the world. Water quality is an essential component sustaining health. This integrated study aimed to assess indexical spatial water quality, potential sources, risks associated with groundwater resources Al-Hassa, Saudi Arabia. Groundwater samples were tested using standard methods. physiochemical results indicated overall pollution. addresses issue drinking resource suitability assessment by introducing innovative approach based on pollution index (PIG). Focusing eastern region Arabia, where management paramount importance, we employed advanced machine learning (ML) models forecast several combinations (C1 = EC + Na Mg Cl, C2 TDS TA HCO3 K Ca, C3 SO4 pH NO3 F Turb). Six ML models, including random forest (RF), decision trees (DT), XgBoost, CatBoost, linear regression, support vector machines (SVM), utilized predict quality. These performance criteria (MAPE, MAE, MSE, DC), offer valuable insights into complex relationships governing accuracy more than 90%. To enhance transparency interpretability incorporated local interpretable model-agnostic explanation method, SHapley Additive exPlanations (SHAP). SHAP allows us interpret prediction-making process otherwise opaque black-box models. We believe that integration SHAP-based explainability offers a promising avenue for sustainable Arabia can serve model addressing similar worldwide. By bridging gap between data-driven predictions actionable insights, this contributes advancement stewardship security region.

Language: Английский

Citations

Integrating machine learning techniques for Air Quality Index forecasting and insights from pollutant-meteorological dynamics in sustainable urban environments DOI

K. Karthick,

Aruna S.K.,

R. Dharmaprakash

et al.

Earth Science Informatics, Journal Year: 2024, Volume and Issue: 17(4), P. 3733 - 3748

Published: June 21, 2024

Language: Английский

Citations