Research on the Parameter Prediction Model for Fully Mechanized Mining Equipment Selection Based on RF-WOA-XGBoost

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(2), P. 732 - 732

Published: Jan. 13, 2025

Fully mechanized mining equipment is core to the coal process. The selection process for this type of complex and heavily relies on experts’ experience determining parameters. This paper proposes a fully parameter prediction model based Extreme Gradient Boosting Regression Trees (XGBoost), which developed mapping relationships among geological parameters, face conditions, parameters equipment. Feature performed feature importance ranking obtained through Random Forest (RF) method, thereby reducing complexity. Different optimization algorithms are used optimize hyperparameters XGBoost, results show that Whale Optimization Algorithm (WOA) outperforms other in terms convergence speed effectiveness. By comparing different algorithms, it found WOA-XGBoost achieves higher accuracy test set, with an average absolute error 0.0458, root mean square 0.1610, coefficient determination (R2) 0.9451. Finally, RF-WOA-XGBoost-based established, suitable lightly inclined faces. reduces input complexity, improves speed, minimizes reliance experts, ensures accuracy, providing effective reference

Language: Английский

High-Resolution Drought Detection Across Contrasting Climate Zones in China

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(7), P. 1169 - 1169

Published: March 26, 2025

Droughts have been exacerbated by climate change, posing significant risks to ecosystems, hydrology, agriculture, and human society. In this paper, we present the development evaluation of a high-resolution 1 km SPEI (Standardized Precipitation-Evapotranspiration Index) dataset enhance drought monitoring at finer spatial scales. The datasets, derived using TPDC precipitation satellite-based MODIS potential evapotranspiration data, were compared with coarse-resolution 50 from CRU measurements, as well vegetation health indices (VHIs) root zone soil moisture (SM), over two climatically contrasting regions in China: Northeast China (NEC) Southwest (SWC). highlights MODIS-based SPEI’s ability capture regional dynamics improved correlation dynamics. NEC, its relatively flat topography recent experience droughts, SWC, characterized complex terrain high variability, provided ideal testbeds for examining performance SPEI. results demonstrate that offered superior detail detecting conditions, making it valuable agricultural planning water resource management diverse climates.

Language: Английский

Prediction of Drought Thresholds Triggering Winter Wheat Yield Losses in the Future Based on the CNN-LSTM Model and Copula Theory: A Case Study of Henan Province

Agronomy, Journal Year: 2025, Volume and Issue: 15(4), P. 954 - 954

Published: April 14, 2025

As global warming progresses, quantifying drought thresholds for crop yield losses is crucial food security and sustainable agriculture. Based on the CNN-LSTM model Copula function, this study constructs a conditional probability framework under future climate change. It analyzes relationship between Standardized Precipitation–Evapotranspiration Index (SPEI) winter wheat yield, assesses vulnerability of in various regions to stress, quantifies The results showed that (1) SPEI Zhoukou, Sanmenxia, Nanyang was significantly correlated with yield; (2) southern eastern higher than center, western, northern past (2000–2023) (2024–2047); (3) there were significant differences thresholds. loss below 30, 50, 70 percentiles (past/future) −1.86/−2.47, −0.85/−1.39, 0.60/0.35 (Xinyang); −1.45/−2.16, −0.75/−1.34, −0.17/−0.43 (Nanyang); −1.47/−2.24, −0.97/−1.61, 0.69/0.28 (Zhoukou); −2.18/−2.86, −1.80/−2.36, −0.75/−1.08 (Kaifeng), indicating threshold will reduce future. This mainly due different soil conditions Henan Province. In context change, droughts be more frequent. Hence, research provide valuable reference efficient utilization agricultural water resources prevention control risk change

Language: Английский