Assessment of hydro energy potential from rain fall data set in India through data analysis DOI Creative Commons
Vikas Khare, Ankita Jain, Miraj Ahmed Bhuiyan

et al.

e-Prime - Advances in Electrical Engineering Electronics and Energy, Journal Year: 2023, Volume and Issue: 6, P. 100290 - 100290

Published: Sept. 29, 2023

The assessment of hydro energy potential is a crucial aspect sustainable planning, particularly in country like India with abundant rainfall and diverse geographical features. This study focuses on assessing the from data sets through analysis. research utilizes comprehensive set patterns across different regions India, considering factors such as spatial distribution, temporal variation, intensity. In this analysis, state considered 1931 to 2022. Various statistical analysis techniques are employed analyze identify inherent patterns. By integrating relevant parameters basin characteristics, topography, hydrological features, holistic understanding derived. includes estimation water availability, area feasibility hydropower projects. According it find out Arunachal Pradesh, Coastal Karnataka, Lakshadweep, Kerala Konkan Goa suitable location for develop more power plant. Based numerical results, also the, Western Ghats, NorthEast Himalayan Region have high average 3,500 - 5,000 (mm), 2,500 4,500 (mm) 1,500 respectively.

Language: Английский

Advanced informatic technologies for intelligent construction: A review DOI
Limao Zhang, Yongsheng Li, Yue Pan

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 137, P. 109104 - 109104

Published: Aug. 29, 2024

Language: Английский

Citations

32
How accurate are the machine learning models in improving monthly rainfall prediction in hyper arid environment? DOI
Faisal Baig, Luqman Ali, Muhammad Abrar Faiz

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 633, P. 131040 - 131040

Published: March 11, 2024

Language: Английский

Citations

28
Enhancing resilience of urban underground space under floods: Current status and future directions DOI

Renfei He,

Robert L. K. Tiong, Yong Yuan

et al.

Tunnelling and Underground Space Technology, Journal Year: 2024, Volume and Issue: 147, P. 105674 - 105674

Published: March 11, 2024

Language: Английский

Citations

23
Prediction of monthly average and extreme atmospheric temperatures in Zhengzhou based on artificial neural network and deep learning models DOI Creative Commons
Qingchun Guo,

Zhenfang He,

Zhaosheng Wang

et al.

Frontiers in Forests and Global Change, Journal Year: 2023, Volume and Issue: 6

Published: Dec. 8, 2023

Introduction Atmospheric temperature affects the growth and development of plants has an important impact on sustainable forest ecological systems. Predicting atmospheric is crucial for management planning. Methods Artificial neural network (ANN) deep learning models such as gate recurrent unit (GRU), long short-term memory (LSTM), convolutional (CNN), CNN-GRU, CNN-LSTM, were utilized to predict change monthly average extreme temperatures in Zhengzhou City. Average data from 1951 2022 divided into training sets (1951–2000) prediction (2001–2022), 22 months used model input next month. Results Discussion The number neurons hidden layer was 14. Six different algorithms, along with 13 various functions, trained compared. ANN evaluated terms correlation coefficient (R), root mean square error (RMSE), absolute (MAE), good results obtained. Bayesian regularization (trainbr) best performing algorithm predicting average, minimum maximum compared other algorithms R (0.9952, 0.9899, 0.9721), showed lowest values RMSE (0.9432, 1.4034, 2.0505), MAE (0.7204, 1.0787, 1.6224). CNN-LSTM performance. This method had generalization ability could be forecast areas. Future climate changes projected using model. temperature, 2030 predicted 17.23 °C, −5.06 42.44 whereas those 2040 17.36 −3.74 42.68 respectively. These suggest that continue warming future.

Language: Английский

Citations

26
Multi-objective optimization for energy-efficient building design considering urban heat island effects DOI
Yan Zhang, Bak Koon Teoh, Limao Zhang

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 376, P. 124117 - 124117

Published: Aug. 16, 2024

Language: Английский

Citations

16
A multi-fidelity deep operator network (DeepONet) for fusing simulation and monitoring data: Application to real-time settlement prediction during tunnel construction DOI Creative Commons
Xu Chen, Ba Trung Cao, Yong Yuan

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 133, P. 108156 - 108156

Published: March 6, 2024

Ground settlement prediction during mechanized tunneling is of paramount importance and remains a challenging research topic. Typically, two paradigms are existing: physics-driven approach utilizing numerical simulation models for prediction, data-driven employing machine learning techniques to learn mappings between influencing factors the settlement. To integrate advantages both approaches assimilate data from different sources, we propose multi-fidelity deep operator network (DeepONet) framework, leveraging recently developed methods. The presented framework comprises components: low-fidelity subnet that captures fundamental ground patterns obtained finite element simulations, high-fidelity learns nonlinear correlation real engineering monitoring data. A pre-processing strategy causality adopted consider spatio-temporal characteristics tunnel excavation. results show proposed method can effectively capture physical information provided by simulations accurately fit measured (R2 around 0.9) as well. Notably, even when dealing with very limited noisy (with 50% error), model robust, achieving satisfactory R2>0.8. In comparison, R2 score pure simulation-based only 0.2. utilization transfer significantly reduces training time 20 min within 30 s, showcasing potential our real-time construction.

Language: Английский

Citations

14
Robust multi-scale time series prediction for building carbon emissions with explainable deep learning DOI
Chao Chen,

Jing Guo,

Limao Zhang

et al.

Energy and Buildings, Journal Year: 2024, Volume and Issue: 312, P. 114159 - 114159

Published: April 7, 2024

Language: Английский

Citations

9
Computational methodologies for critical infrastructure resilience modeling: A review DOI
Ankang Ji,

Renfei He,

Weiyi Chen

et al.

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 62, P. 102663 - 102663

Published: July 4, 2024

Language: Английский

Citations

9
Data-driven optimization for mitigating energy consumption and GHG emissions in buildings DOI
Yan Zhang, Bak Koon Teoh, Limao Zhang

et al.

Environmental Impact Assessment Review, Journal Year: 2024, Volume and Issue: 107, P. 107571 - 107571

Published: June 12, 2024

Language: Английский

Citations

8
Assimilation of PSO and SVR into an improved ARIMA model for monthly precipitation forecasting DOI Creative Commons

Laleh Parviz,

Mansour Ghorbanpour

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: May 27, 2024

Abstract Precipitation due to its complex nature requires a comprehensive model for forecasting purposes and the efficiency of improved ARIMA (IARIMA) forecasts has been proved relative conventional models. This study used two procedures in structure IARIMA obtain accurate monthly precipitation four stations located northern Iran; Bandar Anzali, Rasht, Ramsar, Babolsar. The first procedure applied support vector regression (SVR) modeling statistical characteristics each class, IARIMA-SVR, which evaluation metrics so that decrease Theil's coefficient average variance all was 21.14% 17.06%, respectively. Two approaches are defined second includes forecast combination (C) scheme, IARIMA-C-particle swarm optimization (PSO), artificial intelligence technique. Generally, most time, IARIMA-C-PSO other approach, exhibited acceptable results accuracy improvement greater than zero at stations. Comparing procedures, it is found capability higher concerning normalized mean squared error value from IARIMA-SVR 36.72% 39.92%, respectively residual predictive deviation (RPD) 2, indicates high performance model. With investigation, Anzali station better By developing an model, one can achieve identifying time series issues interest importance.

Language: Английский

Citations

5