Water temperature forecasting based on modified artificial neural network methods: Two cases of the Yangtze River

Qiu, Rujian; Wang, Yuankun; Wang, Dong; Qiu, Wenjie; Wu, Jichun; Tao, Yong

doi:10.1016/j.scitotenv.2020.139729

Cited by 72 publications

(27 citation statements)

References 50 publications

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“…A model behaves efficiently with the source data, conversely, it performs poorly with unknown data. Therefore, it has been highly endorsed that the trained model be tested on an unknown or test dataset to avoid conflicts arising from these problems [ 89 , 90 ]. However, the entire database, on the other hand, has been arbitrarily separated into training sets, validation sets, and testing sets.…”

Section: Methodsmentioning

confidence: 99%

Forecasting Strength of CFRP Confined Concrete Using Multi Expression Programming

et al. 2021

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This study provides the application of a machine learning-based algorithm approach names “Multi Expression Programming” (MEP) to forecast the compressive strength of carbon fiber-reinforced polymer (CFRP) confined concrete. The suggested computational Multiphysics model is based on previously reported experimental results. However, critical parameters comprise both the geometrical and mechanical properties, including the height and diameter of the specimen, the modulus of elasticity of CFRP, unconfined strength of concrete, and CFRP overall layer thickness. A detailed statistical analysis is done to evaluate the model performance. Then the validation of the soft computational model is made by drawing a comparison with experimental results and other external validation criteria. Moreover, the results and predictions of the presented soft computing model are verified by incorporating a parametric analysis, and the reliability of the model is compared with available models in the literature by an experimental versus theoretical comparison. Based on the findings, the valuation and performance of the proposed model is assessed with other strength models provided in the literature using the collated database. Thus the proposed model outperformed other existing models in term of accuracy and predictability. Both parametric and statistical analysis demonstrate that the proposed model is well trained to efficiently forecast strength of CFRP wrapped structural members. The presented study will promote its utilization in rehabilitation and retrofitting and contribute towards sustainable construction material.

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Section: Methodsmentioning

confidence: 99%

Forecasting Strength of CFRP Confined Concrete Using Multi Expression Programming

et al. 2021

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“…(25) Proportion of clean energy graduates with success in innovation and entrepreneurship: the number of successful innovation and entrepreneurship of previous clean energy graduates accounted for the proportion of graduates in the year. (26) Base training clean energy enterprises: college students or teachers have successfully established clean energy enterprises through the entrepreneurial base platform: (27) The improvement of entrepreneurial quality of students majoring in clean energy: the improvement of innovation and entrepreneurship quality and ability of clean energy students receiving innovation and entrepreneurship education in colleges and universities. (28) The proportion of graduates majoring in clean energy in employment: the proportion of self-employed graduates in clean energy major who use entrepreneurship as career planning or entrepreneurial activity to the total number of employed students.…”

Section: Selection Of Evaluation Indexesmentioning

confidence: 99%

“…Compared with BPNN and SVM, GRNN has fewer adjustment parameters, is not easy to fall into a local minimum, and is good at dealing with large-scale training samples [25]. In addition, GRNN has an advantage in forecasting volatile data [26]. Therefore, this paper selected the GRNN model for intelligent evaluation.…”

Section: Introductionmentioning

confidence: 99%

Sustainable Development Evaluation of Innovation and Entrepreneurship Education of Clean Energy Major in Colleges and Universities Based on SPA-VFS and GRNN Optimized by Chaos Bat Algorithm

2021

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The research on the sustainability evaluation of innovation and entrepreneurship education for clean energy majors in colleges and universities can not only cultivate more and better innovative and entrepreneurial talents for the development of sustainable energy but also provide a reference for the sustainable development of innovation and entrepreneurship education for other majors. To achieve systematic and comprehensive scientific evaluation, this paper proposes an evaluation model based on SPA-VFS and Chaos bat algorithm to optimize GRNN. Firstly, the sustainability evaluation index system of innovation and entrepreneurship education for clean energy major in colleges and universities is constructed from the four aspects of the environment, investment, process, and results, and the meaning of each evaluation index is explained; Then, combined with variable fuzzy set evaluation theory (VFS) and set pair analysis theory (SPA), the classical evaluation model based on SPA-VFS is constructed, and the entropy weight method and rank method are coupled to obtain the index weight. The basic bat algorithm is improved by using Tent chaotic mapping, and the chaotic bat algorithm (CBA) is proposed. The generalized regression neural network (GRNN) model is optimized by CBA, and the intelligent evaluation model based on CBA-GRNN is obtained to realize fast real-time calculation; finally, a numerical example is used to verify the scientificity and accuracy of the model proposed in this paper. This study is conducive to a comprehensive evaluation of the sustainability of innovation and entrepreneurship education for clean energy major in colleges and universities, and is conducive to the healthy and sustainable development of innovation and entrepreneurship education for clean energy major in colleges and universities, so as to provide more innovative and entrepreneurial talents for the clean energy industry.

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“…ANN has been identified as one of the robust ML algorithms to capture the nonlinear relationships in the prediction with several applications in various fields of earth science in modeling the near-surface climatology (LeCun et al 2015;Lekkas 2017;Reichstein et al 2019;Qiu et al 2020;Martin et al 2021). ANNs are developed using the human brain and neuron network as inspiration.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Prediction of land surface temperature of major coastal cities of India using bidirectional LSTM neural networks

Maddu

Vanga

Sajja

et al. 2021

Journal of Water and Climate Change

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Surface Temperature (ST) is important in terms of surface energy and terrestrial water balances affecting urban ecosystems. In this study, to process the nonlinear changes of climatological variables by leveraging the distinct advantages of Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (BiLSTM), we propose an LSTM-BiLSTM hybrid deep learning model which extracts multi-dimension features of inputs, i.e., backward (future to past) or forward (past to future) to predict ST. This study assessed the climatological variables, i.e., wind speed, wind direction, relative humidity, dew point temperature, and atmospheric pressure impact on ST using five major coastal cities of India: Chennai, Mangalore, Visakhapatnam, Cuddalore, and Cochin. The Recurrent Neural Networks (RNN) and hybrid LSTM-BiLSTM models have effectively predicted ST and outperformed the standalone Artificial Neural Networks (ANN), LSTM, and BiLSTM models. The RNN and LSTM-BiLSTM models have performed better in predicting ST for Mangalore (Nash-Sutcliffe efficiency (NSE)=0.91), followed by Cochin (NSE=0.89), Chennai (NSE=0.88), Cuddalore (NSE=0.88), and Vishakhapatnam (NSE=0.81). The hybrid data-driven modeling framework indicated that coupling the LSTM and BiLSTM models were proven effective in predicting the ST of coastal cities.

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Water temperature forecasting based on modified artificial neural network methods: Two cases of the Yangtze River

Cited by 72 publications

References 50 publications

Forecasting Strength of CFRP Confined Concrete Using Multi Expression Programming

Forecasting Strength of CFRP Confined Concrete Using Multi Expression Programming

Sustainable Development Evaluation of Innovation and Entrepreneurship Education of Clean Energy Major in Colleges and Universities Based on SPA-VFS and GRNN Optimized by Chaos Bat Algorithm

Prediction of land surface temperature of major coastal cities of India using bidirectional LSTM neural networks

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