Cited by “A Data-driven prediction of residual carbon dioxide under different porous media structures”

Mapping groundwater potential zone by robust machine learning algorithm & remote sing techniques in agriculture dominated area, Bangladesh. DOI

M. M. Shah Porun Rana, Muhammad Tauhidur Rahman, Md. Fuad Hassan

et al.

Cleaner Water, Journal Year: 2025, Volume and Issue: unknown, P. 100064 - 100064

Published: Jan. 1, 2025

Language: Английский

Citations

Improving flood hazard susceptibility assessment by integrating hydrodynamic modeling with remote sensing and ensemble machine learning DOI

Izhar Ahmad,

Rashid Farooq, Muhammad Ashraf

et al.

Natural Hazards, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 11, 2025

Abstract Floods are natural disasters with significant economic and infrastructural impacts. Assessing flood susceptibility in mountainous urban regions is particularly challenging due to the complicated interaction which structures terrain affect behavior. This study employs two ensemble machine learning algorithms, Extreme Gradient Boosting (XGBoost) Random Forest (RF), develop maps for Hunza-Nagar region, has been experiencing frequent flooding past three decades. An unsteady flow simulation carried out HEC-RAS utilizing a 100-year return period hydrograph as an input boundary condition, output of provided spatial inundation extents necessary developing inventory. Ten explanatory factors, including climatic, geological, geomorphological features namely elevation, slope, curvature, topographic wetness index (TWI), normalized difference vegetation (NDVI), land use cover (LULC), rainfall, lithology, distance roads rivers considered mapping. For inventory, random sampling technique adopted create repository non-flood points, incorporating ten geo-environmental conditioning factors. The models’ accuracy assessed using area under curve (AUC) receiver operating characteristics (ROC). prediction rate AUC values 0.912 RF 0.893 XGBoost, also demonstrating superior performance accuracy, precision, recall, F1-score, kappa evaluation metrics. Consequently, model selected represent map area. resulting will assist national disaster management infrastructure development authorities identifying high susceptible zones carrying early mitigation actions future floods.

Language: Английский

Citations

Prediction of Winter Wheat Parameters with Planet SuperDove Imagery and Explainable Artificial Intelligence DOI

Gabriele De Carolis, Vincenzo Giannico, Leonardo Costanza

et al.

Agronomy, Journal Year: 2025, Volume and Issue: 15(1), P. 241 - 241

Published: Jan. 19, 2025

This study investigated the application of high-resolution satellite imagery from SuperDove satellites combined with machine learning algorithms to estimate spatiotemporal variability some winter wheat parameters, including relative leaf chlorophyll content (RCC), water (RWC), and aboveground dry matter (DM). The research was carried out within an experimental field in Southern Italy during 2024 growing season. Different (ML) were trained compared using spectral band data calculated vegetation indices (VIs) as predictors. Model performance assessed R2 RMSE. ML models tested random forest (RF), support vector regressor (SVR), extreme gradient boosting (XGB). RF outperformed other prediction RCC when VIs predictors (R2 = 0.81) RWC DM bands 0.71 0.87, respectively). explainability SHAP method. A analysis highlighted that GNDVI, Cl1, NDRE most important for predicting RCC, while yellow red prediction, nir prediction. best model found each target used its seasonal trend produce a map. approach highlights potential integrating remote monitoring wheat, which can sustainable farming practices.

Language: Английский

Citations

Evaluating empirical and machine learning approaches for reference evapotranspiration estimation using limited climatic variables in Nepal DOI

Erica Shrestha,

Suyog Poudyal,

Anup Ghimire

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104254 - 104254

Published: Feb. 1, 2025

Language: Английский

Citations

A novel hybrid modeling approach based on empirical methods, PSO, XGBoost, and multiple GCMs for forecasting long-term reference evapotranspiration in a data scarce-area DOI

Ali El Bilali, Abdessamad Hadri, Abdeslam Taleb

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 232, P. 110106 - 110106

Published: Feb. 12, 2025

Language: Английский

Citations

ProbML: A Machine Learning‐Based Genome Classifier for Identifying Probiotic Organisms DOI

Arjun Orkkatteri Krishnan,

Lalit Narayan Mudgal,

Vivek Kumar Soni

et al.

Molecular Nutrition & Food Research, Journal Year: 2025, Volume and Issue: unknown

Published: March 26, 2025

Probiotics are microorganisms that offer health benefits to the host. Traditional methods for identifying these organisms time-consuming and resource-intensive. This study addresses need a more efficient accurate approach probiotic identification using machine learning (ML) techniques. The present introduces ProbML, an ML-based from whole genome sequences of prokaryotes. Among five ML algorithms tested, XGBoost models demonstrated superior performance, achieving maximum accuracy 100% on data 95.45% independent test dataset. surpasses existing tools, which achieved 97.77% 66.28% same datasets, respectively. ProbML were used analyze 4728 genomes in Unified Human Gastrointestinal Genome database, classifying 650 as probiotics, with many previously unreported. A versatile GUI platform was also developed employs classification or can be generate custom classifiers based user-specific needs (https://github.com/sysbio-iitmandi/MLG_Dashboard). emphasizes power genomic advanced techniques accelerating discovery.

Language: Английский

Citations

Applications of Long Short-Term Memory (LSTM) Networks in Polymeric Sciences: A Review DOI

Ivan Malashin, В С Тынченко, Andrei Gantimurov

et al.

Polymers, Journal Year: 2024, Volume and Issue: 16(18), P. 2607 - 2607

Published: Sept. 14, 2024

This review explores the application of Long Short-Term Memory (LSTM) networks, a specialized type recurrent neural network (RNN), in field polymeric sciences. LSTM networks have shown notable effectiveness modeling sequential data and predicting time-series outcomes, which are essential for understanding complex molecular structures dynamic processes polymers. delves into use models polymer properties, monitoring polymerization processes, evaluating degradation mechanical performance Additionally, it addresses challenges related to availability interpretability. Through various case studies comparative analyses, demonstrates different science applications. Future directions also discussed, with an emphasis on real-time applications need interdisciplinary collaboration. The goal this is connect advanced machine learning (ML) techniques science, thereby promoting innovation improving predictive capabilities field.

Language: Английский

Citations

Searches for the BSM scenarios at the LHC using decision tree-based machine learning algorithms: a comparative study and review of random forest, AdaBoost, XGBoost and LightGBM frameworks DOI

Arghya Choudhury, Arpita Mondal, Subhadeep Sarkar

et al.

The European Physical Journal Special Topics, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 26, 2024

Language: Английский

Citations

Predicting Cd accumulation in crops and identifying nonlinear effects of multiple environmental factors based on machine learning models DOI

Xiaosong Lu,

Li Sun,

Ya Zhang

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 951, P. 175787 - 175787

Published: Aug. 24, 2024

Language: Английский

Citations

Hybrid deep learning based prediction for water quality of plain watershed DOI

K. H. Wang,

Lei Liu,

Xuechen Ben

et al.

Environmental Research, Journal Year: 2024, Volume and Issue: 262, P. 119911 - 119911

Published: Sept. 2, 2024

Language: Английский

Citations