Hybridization of deep learning, nonlinear system identification and ensemble tree intelligence algorithms for pan evaporation estimation DOI
Gebre Gelete, Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 640, P. 131704 - 131704

Published: July 20, 2024

Language: Английский

Applications of XGBoost in water resources engineering: A systematic literature review (Dec 2018–May 2023) DOI
Majid Niazkar, Andrea Menapace, Bruno Brentan

et al.

Environmental Modelling & Software, Journal Year: 2024, Volume and Issue: 174, P. 105971 - 105971

Published: Feb. 10, 2024

Language: Английский

Citations

77
Predictive modeling of land surface temperature (LST) based on Landsat-8 satellite data and machine learning models for sustainable development DOI

Chaitanya B. Pande,

Johnbosco C. Egbueri, Romulus Costache

et al.

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 444, P. 141035 - 141035

Published: Feb. 8, 2024

Language: Английский

Citations

35
SHAP values accurately explain the difference in modeling accuracy of convolution neural network between soil full-spectrum and feature-spectrum DOI
Liang Zhong, Guo Xi, Meng Ding

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 217, P. 108627 - 108627

Published: Jan. 13, 2024

Language: Английский

Citations

21
Innovative approach for predicting daily reference evapotranspiration using improved shallow and deep learning models in a coastal region: A comparative study DOI

Hussam Eldin Elzain,

Osman Abdalla, Mohammed Abdallah

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 354, P. 120246 - 120246

Published: Feb. 14, 2024

Language: Английский

Citations

18
A Review of Indoor Positioning Systems for UAV Localization with Machine Learning Algorithms DOI Open Access

Chamali Sandamini,

M. W. P. Maduranga,

Valmik Tilwari

et al.

Electronics, Journal Year: 2023, Volume and Issue: 12(7), P. 1533 - 1533

Published: March 24, 2023

The potential of indoor unmanned aerial vehicle (UAV) localization is paramount for diversified applications within large industrial sites, such as hangars, malls, warehouses, production lines, etc. In real-time applications, autonomous UAV location required constantly. This paper comprehensively reviews radio signal-based wireless technologies, machine learning (ML) algorithms and ranging techniques that are used positioning systems. typically relies on vision-based coupled with inertial sensing in Global Positioning System (GPS)-denied situations, visual odometry or simultaneous mapping employing 2D/3D cameras laser rangefinders. work critically the research systems related to mini-UAV environments. It also provides a guide technical comparison perspective different presenting their main advantages disadvantages. Finally, it discusses various open issues highlights future directions localization.

Language: Английский

Citations

29
A hybrid forecasting approach for China's national carbon emission allowance prices with balanced accuracy and interpretability DOI
Yaqi Mao, Xiaobing Yu

Journal of Environmental Management, Journal Year: 2023, Volume and Issue: 351, P. 119873 - 119873

Published: Dec. 29, 2023

Language: Английский

Citations

24
Application of machine learning approaches in supporting irrigation decision making: A review DOI Creative Commons

Lisa Umutoni,

Vidya Samadi

Agricultural Water Management, Journal Year: 2024, Volume and Issue: 294, P. 108710 - 108710

Published: Feb. 9, 2024

Irrigation decision-making has evolved from solely depending on farmers' decisions taken based the visual analysis of field conditions to making crop water need predictions generated using machine learning (ML) techniques. This paper reviews ML related articles discuss how been used enhance irrigation decision making. We reviewed 16 studies that approaches for scheduling prediction and focusing input features, algorithms their applicability in real world conditions. performances terms accuracy, conservation compared fixed or threshold-based methods are discussed along with modeling performances. Informed by research studies, we assessed constraints adoption at scale, which include limited data availability coupled sharing constraints, a lack uncertainty quantification as well physics informed models. To address these limitations, future such integrating process-based models ML, incorporating expert knowledge into procedure, tools Findable, Accessible, Interoperable, Reusable (FAIR). These will improve outcomes boost farm-related FAIRer data-driven applications modeling.

Language: Английский

Citations

15
Evaluation of CatBoost Method for Predicting Weekly Pan Evaporation in Subtropical and Sub-Humid Regions DOI
Dinesh Kumar Vishwakarma, Pankaj Kumar, Krishna Kumar Yadav

et al.

Pure and Applied Geophysics, Journal Year: 2024, Volume and Issue: 181(2), P. 719 - 747

Published: Feb. 1, 2024

Language: Английский

Citations

14
Modelling potential land suitability of large-scale wind energy development using explainable machine learning techniques: Applications for China, USA and EU DOI
Yanwei Sun, Ying Li,

Run Wang

et al.

Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 302, P. 118131 - 118131

Published: Jan. 30, 2024

Language: Английский

Citations

13
Performance prediction of experimental PEM electrolyzer using machine learning algorithms DOI
Safiye Nur Özdemir, Oğuzhan Pektezel

Fuel, Journal Year: 2024, Volume and Issue: 378, P. 132853 - 132853

Published: Aug. 23, 2024

Language: Английский

Citations

12