An integrated framework for prediction and sensitivity analysis of water levels in front of pumping stations DOI Creative Commons
Weilin Wang, Guoqing Sang, Qiang Zhao

et al.

Journal of Hydrology Regional Studies, Journal Year: 2024, Volume and Issue: 57, P. 102119 - 102119

Published: Dec. 13, 2024

Language: Английский

DTTR: Encoding and decoding monthly runoff prediction model based on deep temporal attention convolution and multimodal fusion DOI
Wenchuan Wang,

Wei-can Tian,

Xiao-xue Hu

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 643, P. 131996 - 131996

Published: Sept. 16, 2024

Language: Английский

Citations

14
A Novel Transformer-CNN Approach for Predicting Soil Properties from LUCAS Vis-NIR Spectral Data DOI Creative Commons
Liying Cao, Miao Sun, Zhicheng Yang

et al.

Agronomy, Journal Year: 2024, Volume and Issue: 14(9), P. 1998 - 1998

Published: Sept. 2, 2024

Soil, a non-renewable resource, requires continuous monitoring to prevent degradation and support sustainable agriculture. Visible-near-infrared (Vis-NIR) spectroscopy is rapid cost-effective method for predicting soil properties. While traditional machine learning methods are commonly used modeling Vis-NIR spectral data, large datasets may benefit more from advanced deep techniques. In this study, based on the library LUCAS, we aimed enhance regression model performance in property estimation by combining Transformer convolutional neural network (CNN) techniques predict 11 properties (clay, silt, pH CaCl2, H2O, CEC, OC, CaCO3, N, P, K). The Transformer-CNN accurately predicted most properties, outperforming other (partial least squares (PLSR), random forest (RFR), vector (SVR), Long Short-Term Memory (LSTM), ResNet18) with 10–24 percentage point improvement coefficient of determination (R2). excelled N (R2 = 0.94–0.96, RPD > 3) performed well clay, sand, K 0.77–0.85, 2 < 3). This study demonstrates potential enhancing prediction, although future work should aim optimize computational efficiency explore wider range applications ensure its utility different agricultural settings.

Language: Английский

Citations

6
Enhanced Hyperspectral Forest Soil Organic Matter Prediction Using a Black-Winged Kite Algorithm-Optimized Convolutional Neural Network and Support Vector Machine DOI Creative Commons
Yun Deng, Linsong Xiao, Yuanyuan Shi

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(2), P. 503 - 503

Published: Jan. 7, 2025

Soil Organic Matter (SOM) is crucial for soil fertility, and effective detection methods are of great significance the development agriculture forestry. This study uses 206 hyperspectral samples from state-owned Yachang Huangmian Forest Farms in Guangxi, using SPXY algorithm to partition dataset a 4:1 ratio, provide an spectral data preprocessing method novel SOM content prediction model area similar regions. Three denoising (no denoising, Savitzky–Golay filter discrete wavelet transform denoising) were combined with nine mathematical transformations (original reflectance (R), first-order differential (1DR), second-order (2DR), MSC, SNV, logR, (logR)′, 1/R, ((1/R)′) form 27 combinations. Through Pearson heatmap analysis modeling accuracy comparison, SG-1DR combination was found effectively highlight features. A CNN-SVM based on Black Kite Algorithm (BKA) proposed. leverages powerful parameter tuning capabilities BKA, CNN feature extraction, SVM classification regression, further improving prediction. The results RMSE = 3.042, R2 0.93, MAE 4.601, MARE 0.1, MBE 0.89, PRIQ 1.436.

Language: Английский

Citations

0
Mixture of experts leveraging informer and LSTM variants for enhanced daily streamflow forecasting DOI

Zerong Rong,

Wei Sun, Yutong Xie

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 132737 - 132737

Published: Jan. 1, 2025

Language: Английский

Citations

0
Advanced groundwater level forecasting with hybrid deep learning model: Tackling water challenges in Taiwan’s largest alluvial fan DOI

Yu-Wen Chang,

Wei Sun, Pu-Yun Kow

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 132887 - 132887

Published: Feb. 1, 2025

Language: Английский

Citations

0
Integral delay inspired deep learning model for single pool water level prediction DOI
Xiaohui Lei, Jiahao Wu, Yan Long

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 133328 - 133328

Published: April 1, 2025

Language: Английский

Citations

0
A novel approach to forecast water table rise in arid regions using stacked ensemble machine learning and deep artificial intelligence models DOI

Hussam Eldin Elzain,

Osman Abdalla, Ali Al‐Maktoumi

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 640, P. 131668 - 131668

Published: July 17, 2024

Language: Английский

Citations

1
A novel reservoir dispatching rules extraction framework based on hybrid embedding informer DOI
Shuai Liu, Hui Qin,

Zhengyang Tang

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: unknown, P. 132047 - 132047

Published: Sept. 1, 2024

Language: Английский

Citations

1
An integrated framework for prediction and sensitivity analysis of water levels in front of pumping stations DOI Creative Commons
Weilin Wang, Guoqing Sang, Qiang Zhao

et al.

Journal of Hydrology Regional Studies, Journal Year: 2024, Volume and Issue: 57, P. 102119 - 102119

Published: Dec. 13, 2024

Language: Английский

Citations

0