Daily Streamflow Forecasting Using Networks of Real-Time Monitoring Stations and Hybrid Machine Learning Methods DOI Open Access
Yue Zhang, Zimo Zhou, Ying Deng

et al.

Water, Journal Year: 2024, Volume and Issue: 16(9), P. 1284 - 1284

Published: April 30, 2024

Considering the increased risk of urban flooding and drought due to global climate change rapid urbanization, imperative for more accurate methods streamflow forecasting has intensified. This study introduces a pioneering approach leveraging available network real-time monitoring stations advanced machine learning algorithms that can accurately simulate spatial–temporal problems. The Spatio-Temporal Attention Gated Recurrent Unit (STA-GRU) model is renowned its computational efficacy in events with forecast horizon 7 days. novel integration groundwater level, precipitation, river discharge as predictive variables offers holistic view hydrological cycle, enhancing model’s accuracy. Our findings reveal 7-day period, STA-GRU demonstrates superior performance, notable improvement mean absolute percentage error (MAPE) values R-square (R2) alongside reductions root squared (RMSE) (MAE) metrics, underscoring generalizability reliability. Comparative analysis seven conventional deep models, including Long Short-Term Memory (LSTM), Convolutional Neural Network LSTM (CNNLSTM), (ConvLSTM), (STA-LSTM), (GRU), GRU (CNNGRU), STA-GRU, confirms power STA-LSTM models when faced long-term prediction. research marks significant shift towards an integrated deep-learning forecasting, emphasizing importance spatially temporally encompassing variability within watershed’s stream network.

Language: Английский

Urban inundation rapid prediction method based on multi-machine learning algorithm and rain pattern analysis DOI
Guangzhao Chen, Jingming Hou, Yuan Liu

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 633, P. 131059 - 131059

Published: March 8, 2024

Language: Английский

Citations

16
Optimization of 2024-T3 Aluminum Alloy Friction Stir Welding Using Random Forest, XGBoost, and MLP Machine Learning Techniques DOI Open Access
Piotr Myśliwiec, Andrzej Kubit, Paulina Szawara

et al.

Materials, Journal Year: 2024, Volume and Issue: 17(7), P. 1452 - 1452

Published: March 22, 2024

This study optimized friction stir welding (FSW) parameters for 1.6 mm thick 2024T3 aluminum alloy sheets. A 3 × factorial design was employed to explore tool rotation speeds (1100 1300 rpm) and (140 180 mm/min). Static tensile tests revealed the joints' maximum strength at 87% relative base material. Hyperparameter optimization conducted machine learning (ML) models, including random forest XGBoost, multilayer perceptron artificial neural network (MLP-ANN) using grid search. Welding parameter extrapolation were then carried out, with final predictions analyzed response surface methodology (RSM). The ML models achieved over 98% accuracy in regression, demonstrating significant effectiveness FSW process enhancement. Experimentally validated, resulted an joint efficiency of 93% outcome highlights critical role advanced analytical techniques improving quality efficiency.

Language: Английский

Citations

16
Advancements in drought using remote sensing: assessing progress, overcoming challenges, and exploring future opportunities DOI
Vijendra Kumar, Kul Vaibhav Sharma, Quoc Bao Pham

et al.

Theoretical and Applied Climatology, Journal Year: 2024, Volume and Issue: 155(6), P. 4251 - 4288

Published: March 5, 2024

Language: Английский

Citations

11
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

10
Multi-Criteria Assessment of Flood Risk on Railroads Using a Machine Learning Approach: A Case Study of Railroads in Minas Gerais DOI Creative Commons
Fernanda Oliveira de Sousa, Victor Andre Ariza Flores, Christhian Santana Cunha

et al.

Infrastructures, Journal Year: 2025, Volume and Issue: 10(1), P. 12 - 12

Published: Jan. 8, 2025

In a climate change scenario where extreme precipitation events occur more frequently and intensely, risk assessment plays critical role in ensuring the safety operational efficiency of facilities. This case study uses combination multi-criteria analysis approach hydrological studies that use machine learning algorithms to simulate new rainfall order estimate flooding on railroads. Risk variables, including terrain, drainage capability, accumulated flow, land cover, will be weighed using multicriteria approach. A methodical evaluation most vulnerable locations railroad network possible thanks these parameters based geographic information system (GIS) meantime, historical precipitation, balance data used calibrate validate models. The database required for model can created with data. research regions are situated densely rail-networked state Minas Gerais. geographical climatic diversity Gerais makes it perfect place test suggested approaches. models evaluated included linear regression, random forest, decision tree, support vector machines. Among models, Linear Regression emerged as best-performing an R2 value 0.999998, mean squared error (MSE) 0.018672, low tendency overfitting (0.000011).

Language: Английский

Citations

1
Predicting water level fluctuations in glacier-fed lakes by ensembling individual models into a quad-meta model DOI Creative Commons

Shoukat Ali Shah,

Songtao Ai, Huanxin Yuan

et al.

Engineering Applications of Computational Fluid Mechanics, Journal Year: 2025, Volume and Issue: 19(1)

Published: Jan. 8, 2025

Predicting water levels in glacier-fed lakes is vital for resource management, flood forecasting, and ecological balance. This study examines the predictive capacity of multiple climate factors affecting Blue Moon Lake Valley, fed by Baishui River glacier on Yulong Snow Mountain. The introduces a novel quad-meta (QM) ensemble model that integrates outputs from four machine learning models – extreme gradient boosting (XGB), random forest (RF), (GBM), decision tree (DT) through meta-learning to improve prediction accuracy under complex environmental conditions. High-frequency depth data, recorded every five minutes using an RBR logger, alongside variables such as temperature, wind speed, humidity, evaporation, solar radiation, rainfall, were analyzed. Temperature was identified most significant factor influencing levels, with importance score 15.69, followed atmospheric pressure (14.08) radiation (12.89), which impacted surface conditions evaporation. Relative humidity (10.24) speed (8.71) influenced lake stability mixing. QM outperformed individual models, achieved RMSE values 0.003 m (climate data) 0.001 (water data), R2 0.994 0.999, respectively. In comparison, XGB GBM exhibited higher lower scores. RF struggled 0.008 0.962, while DT performed better (RMSE: 0.006 but remained inferior proposed model. These findings demonstrate robustness approach handling particularly where fall short. highlights potential enhanced systems, recommending future research directions incorporate deep long-term forecasting expand capabilities global scale.

Language: Английский

Citations

1
A hybrid Two Stage Taguchi-Regression-NSGA II-AHP-GRA, Multi-Objective Optimization Framework for Sustainable Straight Slot Milling of AZ31 Magnesium Alloy DOI Creative Commons

M. Atif Saeed,

Faraz Junejo, Imran Amin

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: 25, P. 104451 - 104451

Published: Feb. 22, 2025

Language: Английский

Citations

1
A Comparison of Machine Learning Models for Predicting Rainfall in Urban Metropolitan Cities DOI Open Access
Vijendra Kumar, Naresh Kedam, Kul Vaibhav Sharma

et al.

Sustainability, Journal Year: 2023, Volume and Issue: 15(18), P. 13724 - 13724

Published: Sept. 14, 2023

Current research studies offer an investigation of machine learning methods used for forecasting rainfall in urban metropolitan cities. Time series data, distinguished by their temporal complexities, are exploited using a unique data segmentation approach, providing discrete training, validation, and testing sets. Two models created: Model-1, which is based on daily Model-2, weekly data. A variety performance criteria to rigorously analyze these models. CatBoost, XGBoost, Lasso, Ridge, Linear Regression, LGBM among the algorithms under consideration. This study provides insights into predictive abilities, revealing significant trends across phases. The results show that ensemble-based algorithms, particularly CatBoost outperform both emerged as model choice throughout all assessment stages, including testing. MAE was 0.00077, RMSE 0.0010, RMSPE 0.49, R2 0.99, confirming CatBoost’s unrivaled ability identify deep intricacies within patterns. Both had indicating remarkable predict trends. Significant XGBoost included 0.02 0.10, handle longer time intervals. Regression varies. Scatter plots demonstrate robustness demonstrating capacity sustain consistently low prediction errors dataset. emphasizes potential transform meteorology planning, improve decision-making through precise forecasts, contribute disaster preparedness measures.

Language: Английский

Citations

22
Performance of machine learning models to forecast PM10 levels DOI Creative Commons
Lakindu Mampitiya, Namal Rathnayake, Yukinobu Hoshino

et al.

MethodsX, Journal Year: 2024, Volume and Issue: 12, P. 102557 - 102557

Published: Jan. 5, 2024

Machine learning techniques have garnered considerable attention in modern technologies due to their promising outcomes across various domains. This paper presents the comprehensive methodology of an optimized and efficient forecasting approach for Particulate Matter 10, specifically tailored predefined locations. The execution a comparative analysis involving eight models enables identification most suitable model that aligns with primary research objective. Notably, test results underscore superior performance ensemble model, which integrates state-of-the-art methodologies, surpassing other seven models. Adopting case-specific machine contributes achieving notably high regression coefficient (R²≈1) all Furthermore, study underscores potential future endeavors predicting location-specific environmental factors.•This focused on PM10 consideration air quality factors meteorological factors•Ensemble was developed purposes higher performance.Graphical abstract

Language: Английский

Citations

8
Optimizing flood predictions by integrating LSTM and physical-based models with mixed historical and simulated data DOI Creative Commons
Jun Li, Guofang Wu,

Yongpeng Zhang

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(13), P. e33669 - e33669

Published: June 27, 2024

The current flood forecasting models heavily rely on historical measured data, which is often insufficient for robust predictions due to practical challenges such as high measurement costs and data scarcity. This study introduces a novel hybrid approach that synergistically combines the outputs of traditional physical-based with train Long Short-Term Memory (LSTM) networks. Specifically, NAM hydrological model HD hydraulic are employed simulate processes. Focusing Jinhua basin, typical plains river area in China, this research evaluates efficacy LSTM trained measured, mixed, simulated datasets. architecture includes multiple layers, optimized hyperparameters tailored forecasting. Key performance indicators Root Mean Square Error (RMSE), Absolute (MAE), Peak-relative (PRE) assess predictive accuracy models. findings demonstrate mixed datasets simulated-to-measured ratio less than 2:1 consistently achieve superior performance, exhibiting significantly lower RMSE MAE values compared larger ratios. highlights advantage integrating leveraging strengths both types enhance accuracy. Despite its advantages, has limitations, including dependence quality potential computational complexity. However, development marks significant advancement forecasting, offering promising solution efficiency Potential applications include real-time prediction risk management other flood-prone regions, providing framework diverse sources improve

Language: Английский

Citations

8