Transmission Line Fault Diagnosis Method Based on SDA-ISSA-XGBoost under Meteorological Factors DOI Open Access
Kun Zhang

Journal of Physics Conference Series, Год журнала: 2023, Номер 2666(1), С. 012006 - 012006

Опубликована: Дек. 1, 2023

Abstract Transmission lines are directly exposed to the natural environment and prone failure due meteorological factors. A novel approach for diagnosing transmission line faults under various conditions has been introduced. This method, known as SDA-ISSA-XGBoost, combines power of Stacked Denoising Autoencoder (SDA), an improved Sparrow Search Algorithm (ISSA) enhanced with chaotic mapping sequences, adaptive weights, iterative local search, a random differential mutation strategy, eXtreme Gradient Boosting (XGBoost). The process begins SDA, which extracts essential features from initial data. Subsequently, ISSA is applied optimize parameters XGBoost model. results in ISSA-XGBoost fault diagnosis performance this model compared PSO-XGBoost SSA-XGBoost. experimental findings demonstrate that achieves impressive accuracy 94.39%, surpassing both SSA-XGBoost by 6.54% 3.74%, respectively.

Язык: Английский

Modern computational approaches for rice yield prediction: A systematic review of statistical and machine learning-based methods DOI
Djavan De Clercq, Adam Mahdi

Computers and Electronics in Agriculture, Год журнала: 2025, Номер 231, С. 109852 - 109852

Опубликована: Фев. 5, 2025

Язык: Английский

Процитировано

2
Construction and Optimization of Integrated Yield Prediction Model Based on Phenotypic Characteristics of Rice Grown in Small–Scale Plantations DOI Creative Commons

Jihong Sun,

Peng Tian,

Zhaowen Li

и другие.

Agriculture, Год журнала: 2025, Номер 15(2), С. 181 - 181

Опубликована: Янв. 15, 2025

An intelligent prediction model for rice yield in small-scale cultivation areas can provide precise forecasting results farmers, planting enterprises, and researchers, holding significant importance agricultural industries crop science research within small regions. Although machine learning handle complex nonlinear problems to enhance accuracy, further improvements models are still needed accurately predict yields facing environments, thereby enhancing performance. This study employs four phenotypic traits, namely, panicle angle, length, total branch grain number, along with seven methods—multiple linear regression, support vector machine, MLP, random forest, GBR, XGBoost, LightGBM—to construct a group. Subsequently, the top three best performance individual predictions integrated using voting stacking ensemble methods obtain optimal model. Finally, impact of different traits on stacked is explored. Experimental indicate that forest performs after modeling, RMSE, R2, MAPE values 0.2777, 0.9062, 17.04%, respectively. After integration, Stacking–3m demonstrates performance, 0.2483, 0.9250, 6.90%, Compared RMSE decreased by 10.58%, R2 increased 1.88%, 0.76%, indicating improved ensemble. The model, which demonstrated comprehensive evaluation metrics, was selected validation, validation were satisfactory, MAE, 8.3384, 0.9285, 0.2689, above findings demonstrate this possesses high practical value fills gap Yunnan Plateau region.

Язык: Английский

Процитировано

1
Yield estimation from SAR data using patch-based deep learning and machine learning techniques DOI

Mahya G.Z. Hashemi,

Pang‐Ning Tan, Ehsan Jalilvand

и другие.

Computers and Electronics in Agriculture, Год журнала: 2024, Номер 226, С. 109340 - 109340

Опубликована: Авг. 29, 2024

Язык: Английский

Процитировано

8
Feasibility of machine learning-based rice yield prediction in India at the district level using climate reanalysis and remote sensing data DOI
Djavan De Clercq, Adam Mahdi

Agricultural Systems, Год журнала: 2024, Номер 220, С. 104099 - 104099

Опубликована: Авг. 29, 2024

Язык: Английский

Процитировано

8
Analysis and forecasting of Australian rice yield using phenology-based aggregation of satellite and weather data DOI Creative Commons
James Brinkhoff, Allister Clarke, Brian W. Dunn

и другие.

Agricultural and Forest Meteorology, Год журнала: 2024, Номер 353, С. 110055 - 110055

Опубликована: Май 18, 2024

Rice yield depends on factors including variety, weather, field management, nutrient and water availability. We analyzed important drivers of variability at the scale, developed forecast models for crops in temperate irrigated rice growing region Australia. fused a time-series Sentinel-1 Sentinel-2 satellite remote sensing imagery, spatial weather data management information. phenology was predicted using previously reported models. Higher yields were associated with early flowering, higher chlorophyll indices temperatures around flowering. Successive cropping same lower (p<0.001). After running series leave-one-year-out cross validation experiments, final trained 2018–2022 data, applied to predicting 1580 fields (43,700 hectares) from an independent season challenging conditions (2023). Models which aggregated phenological periods provided more accurate predictions than that these predictors calendar periods. The accuracy improved as progressed, reaching RMSE=1.6 t/ha Lin's concordance correlation coefficient (LCCC) 0.67 30 days after flowering level. Explainability SHAP method, revealing likely overall, individual fields.

Язык: Английский

Процитировано

7
Review of synthetic aperture radar with deep learning in agricultural applications DOI

Mahya G.Z. Hashemi,

Ehsan Jalilvand, Hamed Alemohammad

и другие.

ISPRS Journal of Photogrammetry and Remote Sensing, Год журнала: 2024, Номер 218, С. 20 - 49

Опубликована: Сен. 10, 2024

Язык: Английский

Процитировано

7
Wheat Yield Robust Prediction in the Huang-Huai-Hai Plain by Coupling Multi-Source Data with Ensemble Model under Different Irrigation and Extreme Weather Events DOI Creative Commons
Yanxi Zhao, Jiaoyang He, Xia Yao

и другие.

Remote Sensing, Год журнала: 2024, Номер 16(7), С. 1259 - 1259

Опубликована: Апрель 2, 2024

The timely and robust prediction of wheat yield is very significant for grain trade food security. In this study, the model was developed by coupling an ensemble with multi-source data, including vegetation indices (VIs) meteorological data. results showed that green chlorophyll index (GCVI) optimal remote sensing (RS) variable predicting compared other VIs. accuracy adaptive boosting- long short-term memory (AdaBoost-LSTM) higher than LSTM model. AdaBoost-LSTM coupled input data had best performance. strong robustness under different irrigation extreme weather events in general. Additionally, rainfed counties most years except years. characteristic variables window from February to April smaller requirements, which window. Therefore, can be accurately predicted one two months lead time before maturity HHHP. Overall, achieve accurate large-scale regions.

Язык: Английский

Процитировано

4
Predicting wheat yield from 2001 to 2020 in Hebei Province at county and pixel levels based on synthesized time series images of Landsat and MODIS DOI Creative Commons

Guanjin Zhang,

Siti Nur Aliaa Roslan, Helmi Zulhaidi Mohd Shafri

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Июль 13, 2024

Abstract To obtain seasonable and precise crop yield information with fine resolution is very important for ensuring the food security. However, quantity quality of available images selection prediction variables often limit performance prediction. In our study, synthesized Landsat MODIS were used to provide remote sensing (RS) variables, which can fill missing values well cover study area completely. The deep learning (DL) was combine different vegetation index (VI) climate data build wheat model in Hebei Province (HB). results showed that kernel NDVI (kNDVI) near-infrared reflectance (NIRv) slightly outperform normalized difference (NDVI) And regression algorithm had a more prominent effect on prediction, while using Long Short-Term Memory (LSTM) outperformed Light Gradient Boosting Machine (LGBM). combining LSTM NIRv best relatively stable single year. optimal then generate 30 m maps past 20 years, higher overall accuracy. addition, we define optimum time at April, consider simultaneously lead time. general, expect this understand ensure

Язык: Английский

Процитировано

4
HIDYM: A high-resolution gross primary productivity and dynamic harvest index based crop yield mapper DOI
Weiguo Yu, Dong Li, Hengbiao Zheng

и другие.

Remote Sensing of Environment, Год журнала: 2024, Номер 311, С. 114301 - 114301

Опубликована: Июль 2, 2024

Язык: Английский

Процитировано

3
Rice yield prediction through integration of biophysical parameters with SAR and optical remote sensing data using machine learning models DOI Creative Commons

Sonam Sah,

Dipanwita Haldar,

RN Singh

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Сен. 17, 2024

Abstract In an era marked by growing global population and climate variability, ensuring food security has become a paramount concern. Rice, being staple crop for billions of people, requires accurate timely yield prediction to ensure security. This study was undertaken across two rice seasons in the Udham Singh Nagar district Uttarakhand state predict at 45, 60 90 days after transplanting (DAT) through machine learning (ML) models, utilizing combination optical Synthetic Aperture Radar (SAR) data conjunction with biophysical parameters. Results revealed that ML models were able provide relatively early estimates. For summer rice, eXtreme gradient boosting (XGB) best-performing model all three stages (45, 60, DAT), while kharif DAT XGB, Neural network (NNET), Cubist, respectively. The combined ranking showed accuracy improved as date approaches harvest, best observed both rice. Overall rankings indicate top NNET, Support vector regression, these Random Forest, findings this offer valuable insights into potential use remote sensing parameters using which enhances planning resource management enabling more informed decision-making stakeholders such farmers, policy planners well researchers.

Язык: Английский

Процитировано

3