Spatial Prediction of Soil Water Content by Bayesian Optimization–Deep Forest Model with Landscape Index and Soil Texture Data DOI Creative Commons

Weihao Yang,

Ruohan Zhen,

Fanxiang Meng

и другие.

Agronomy, Год журнала: 2024, Номер 14(12), С. 3039 - 3039

Опубликована: Дек. 19, 2024

The accurate prediction of the spatial variability for soil water content (SWC) in farmland is essential resource management and sustainable agricultural development. However, natural factors introduce uncertainty result poor alignment when predicting SWC, leading to low accuracy. To address this, this study introduced a novel indicator: landscape indices. These indices include largest patch index (LPI), edge density (ED), aggregation (AI), cohesion (COH), contagion (CON), division (DIV), percentage like adjacencies (PLA), Shannon evenness (SHEI), diversity (SHDI). A Bayesian optimization–deep forest (BO–DF) model was developed leverage these SWC. Statistical analysis revealed that exhibited skewed distributions weak linear correlations with SWC (r < 0.2). Despite incorporating variables into BO–DF significantly improved accuracy, R2 increasing by 35.85%. This demonstrated robust nonlinear fitting capability Spatial mapping using indicated high-value areas were predominantly located eastern southern regions Yellow River Delta China. Furthermore, SHapley additive explanation (SHAP) highlighted key drivers findings underscore potential as valuable prediction, supporting regional strategies

Язык: Английский

Microplastic removal, identification and characterization in Chennai sewage treatment plants DOI

Igor Roy,

A. Stanley Raj,

Stefano Viaroli

и другие.

Journal of Environmental Management, Год журнала: 2025, Номер 380, С. 125120 - 125120

Опубликована: Март 28, 2025

Язык: Английский

Процитировано

0
Deposition rates and air concentrations of tire and road wear particles near a motorway in Germany DOI Creative Commons
Stephan Weinbruch,

J Matthies,

Linyue Zou

и другие.

Atmospheric Environment, Год журнала: 2025, Номер unknown, С. 121228 - 121228

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
Soil properties explain the variability in tire wear particle effects in soil based on a laboratory test with 59 soils DOI

Tingting Zhao,

Yaqi Xu, Mohan Bi

и другие.

Environmental Pollution, Год журнала: 2025, Номер unknown, С. 126271 - 126271

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
Determination of Drip Irrigation Timing and Duration Based on Soil Water Wetting Characteristics: A Case Study for Tea Field Located at Yangtze River Region DOI
Yongzong Lu,

Wuzhe Wei,

Yongguang Hu

и другие.

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

0
Spatial Prediction of Soil Water Content by Bayesian Optimization–Deep Forest Model with Landscape Index and Soil Texture Data DOI Creative Commons

Weihao Yang,

Ruohan Zhen,

Fanxiang Meng

и другие.

Agronomy, Год журнала: 2024, Номер 14(12), С. 3039 - 3039

Опубликована: Дек. 19, 2024

The accurate prediction of the spatial variability for soil water content (SWC) in farmland is essential resource management and sustainable agricultural development. However, natural factors introduce uncertainty result poor alignment when predicting SWC, leading to low accuracy. To address this, this study introduced a novel indicator: landscape indices. These indices include largest patch index (LPI), edge density (ED), aggregation (AI), cohesion (COH), contagion (CON), division (DIV), percentage like adjacencies (PLA), Shannon evenness (SHEI), diversity (SHDI). A Bayesian optimization–deep forest (BO–DF) model was developed leverage these SWC. Statistical analysis revealed that exhibited skewed distributions weak linear correlations with SWC (r < 0.2). Despite incorporating variables into BO–DF significantly improved accuracy, R2 increasing by 35.85%. This demonstrated robust nonlinear fitting capability Spatial mapping using indicated high-value areas were predominantly located eastern southern regions Yellow River Delta China. Furthermore, SHapley additive explanation (SHAP) highlighted key drivers findings underscore potential as valuable prediction, supporting regional strategies

Язык: Английский

Процитировано

0