An interpretable Bayesian deep learning-based approach for sustainable clean energy DOI Creative Commons
Dalia Ezzat,

Eman Ahmed,

Mona Soliman

и другие.

Neural Computing and Applications, Год журнала: 2024, Номер 36(27), С. 17145 - 17163

Опубликована: Июнь 6, 2024

Abstract Sustainable Development Goal 7 is dedicated to ensuring access clean and affordable energy that can be utilized in various applications. Solar panels (SP) are convert sunlight into electricity, acting as a renewable source. It important keep SP obtain the required performance, accumulation of snow dust on greatly affects amount electricity generated. On other hand, excessive cleaning has some detrimental effects SP, therefore should only done when necessary not regular basis. Consequently, it critical determine whether procedure by automatically detecting presence or while avoiding inaccurate predictions. Research efforts have been made detect but most proposed methods do guarantee accurate detection results. This paper proposes an accurate, reliable, interpretable approach called Solar-OBNet. The Solar-OBNet dusty snow-covered very efficiently used conjunction with SP. based Bayesian convolutional neural network, which enables express confidence its Two measurements estimate uncertainty outcomes Solar-OBNet, namely predictive entropy standard deviation. correct predictions showing low values for also give warning case erroneous high Solar-OBNet’s efficacy was verified interpreting results using method Weighted Gradient-Directed Class Activation Mapping (Grad-CAM). achieved balanced accuracy 94.07% average specificity 95.83%, outperforming comparable methods.

Язык: Английский

Enhancing flood susceptibility mapping in Meghna River basin by introducing ensemble Naive Bayes with stacking algorithms DOI Creative Commons
Abu Reza Md. Towfiqul Islam,

Md. Uzzal Mia,

Nílson Augusto Villa Nova

и другие.

Geomatics Natural Hazards and Risk, Год журнала: 2025, Номер 16(1)

Опубликована: Фев. 13, 2025

Язык: Английский

Процитировано

1
Flood and Non-Flood Image Classification using Deep Ensemble Learning DOI

Ellora Yasi,

Tasnim Ullah Shakib,

Nusrat Sharmin

и другие.

Water Resources Management, Год журнала: 2024, Номер unknown

Опубликована: Июнь 7, 2024

Язык: Английский

Процитировано

4
Reservoir-based flood forecasting and warning: deep learning versus machine learning DOI Creative Commons
Sooyeon Yi, Jaeeung Yi

Applied Water Science, Год журнала: 2024, Номер 14(11)

Опубликована: Окт. 15, 2024

In response to increasing flood risks driven by the climate crisis, urban areas require advanced forecasting and informed decision-making support sustainable development. This study seeks improve reliability of reservoir-based ensure adequate lead time for effective measures. The main objectives are predict hourly downstream discharge at a reference point, compare predictions from single reservoir with four-hour against those three reservoirs seven-hour time, evaluate accuracy data-driven approaches. takes place in Han River Basin, located Seoul, South Korea. Approaches include two non-deep learning (NDL) (random forest (RF), vector regression (SVR)) deep (DL) (long short-term memory (LSTM), gated recurrent unit (GRU)). Scenario 1 incorporates data reservoirs, while 2 focuses solely on Paldang reservoir. Results show that RF performed 4.03% (in R2) better than SVR, GRU 4.69% LSTM 1. 2, none models showed any outstanding performance. Based these findings, we propose two-step approach: Initial should utilize upstream long closer event, model focus more accurate prediction. work stands as significant contribution, making well-timed local administrations issue warnings execute evacuations mitigate damage casualties areas.

Язык: Английский

Процитировано

4
Development of risk maps for flood, landslide, and soil erosion using machine learning model DOI

Narges Javidan,

Ataollah Kavian, Christian Conoscenti

и другие.

Natural Hazards, Год журнала: 2024, Номер unknown

Опубликована: Июнь 9, 2024

Язык: Английский

Процитировано

3
Exploring the spatial association characteristics of carbon emission efficiency in China’s construction industry: A network perspective DOI
Fangliang Wang, Qi Zhang

Energy and Buildings, Год журнала: 2025, Номер 329, С. 115289 - 115289

Опубликована: Янв. 11, 2025

Язык: Английский

Процитировано

0
Data Uncertainty of Flood Susceptibility Using Non-Flood Samples DOI Creative Commons

Y. Zhang,

Yongqiang Wei,

Rui Yao

и другие.

Remote Sensing, Год журнала: 2025, Номер 17(3), С. 375 - 375

Опубликована: Янв. 23, 2025

Flood susceptibility provides scientific support for flood prevention planning and infrastructure development by identifying assessing flood-prone areas. The uncertainty posed non-flood sample datasets remains a key challenge in mapping. Therefore, this study proposes novel sampling method points. A model is constructed using machine learning algorithm to examine the due point selection. influencing factors of are analyzed through interpretable models. Compared generated random with buffer method, dataset spatial range identified frequency ratio one-class vector achieves higher accuracy. This significantly improves simulation accuracy model, an increase 24% ENSEMBLE model. (2) In constructing optimal dataset, demonstrates than other methods, AUC 0.95. (3) northern southeastern regions Zijiang River Basin have extremely high susceptibility. Elevation drainage density as causing these areas, whereas southwestern region exhibits low elevation. (4) Elevation, slope, three most important affecting Lower values elevation slope correlate offers new approach reducing technical disaster mitigation basin.

Язык: Английский

Процитировано

0
Predicting flood risks using advanced machine learning algorithms with a focus on Bangladesh: influencing factors, gaps and future challenges DOI
Abu Reza Md. Towfiqul Islam,

Md. Jannatul Naeem Jibon,

Md. Abubakkor Siddik

и другие.

Earth Science Informatics, Год журнала: 2025, Номер 18(3)

Опубликована: Фев. 27, 2025

Язык: Английский

Процитировано

0
The suspension load deviation control method for high-power locomotives via Harris Hawk optimization DOI
Yuqi Xiao, Yongjun Wu

ISA Transactions, Год журнала: 2025, Номер unknown

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Flood susceptibility assessment using deep neural networks and open-source spatial datasets in transboundary river basin DOI
Huu Duy Nguyen, Dinh Kha Dang,

H Truong

и другие.

VIETNAM JOURNAL OF EARTH SCIENCES, Год журнала: 2025, Номер unknown

Опубликована: Апрель 16, 2025

The Mekong Basin is the most critical transboundary river basin in Asia. This provides an abundant source of fresh water essential for development agriculture, domestic consumption, and industry, as well production hydroelectricity, it also contributes to ensuring food security worldwide. region often subject floods that cause significant damage human life, society, economy. However, flood risk management challenges this are increasingly substantial due conflicting objectives between several countries data sharing. study integrates deep learning with optimization algorithms, namely Grasshopper Optimisation Algorithm (GOA), Adam Stochastic Gradient Descent (SGD), open-source datasets identify probably occurring basin, covering Vietnam Cambodia. Various statistical indices, Area Under Curve (AUC), root mean square error (RMSE), absolute (MAE), coefficient determination (R²), were used evaluate susceptibility models. results show proposed models performed AUC values above 0.8, specifying DNN-Adam model achieved 0.98, outperforming DNN-GOA (AUC = 0.89), DNN-SGD 0.87), XGB 0.82. Regions very high concentrated Delta along River findings supporting decision-makers or planners proposing appropriate mitigation strategies, planning policies, particularly watershed.

Язык: Английский

Процитировано

0
Automatic Calibration and Inversion of SWMM Parameters Using HHO-BP Algorithm DOI
Xiaohan Ma, Fan Chen,

Zhikai Cai

и другие.

Lecture notes in civil engineering, Год журнала: 2025, Номер unknown, С. 171 - 184

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0