Evolutionary artificial intelligence model via cooperation search algorithm and extreme learning machine for multiple scales nonstationary hydrological time series prediction

Feng, Zhong-kai; Niu, Wen-jing; Tang, Zhengyang; Xu, Yuebin; Zhang, Hairong

doi:10.1016/j.jhydrol.2021.126062

Cited by 54 publications

(20 citation statements)

References 78 publications

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“…Musical intelligence is the ability to perceive, distinguish, change, and express musical forms (Fadilloh et al, 2021;Hilyana & Khotimah, 2021;Shahzada et al, 2021). Musical intelligence is the ability to develop, express, and enjoy forms of music and sound, such as sensitivity to rhythm, melody, and notation, playing musical instruments, singing and composing songs, music, and songs (Feng et al, 2021;Yavich & Rotnitsky, 2020). In this study, the learning steps that can improve musical intelligence are Solve the Problem on Outdoor and Unity on Role Play.…”

Section: Discussionmentioning

confidence: 99%

“…Naturalist intelligence is a person's ability to identify and classify natural patterns (nature). Naturalist intelligence can also be interpreted as the ability to categorize and create hierarchies of the state of organisms such as plants, animals, and nature (Fadilloh et al, 2021;Feng et al, 2021;Tang, 2021;Viana et al, 2019). Learning activities developed to improve naturalist intelligence are Solve the problem on Outdoor and Actualization on solutions.…”

Section: Discussionmentioning

confidence: 99%

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Learning Model Gawi Sabumi Based on Local Wisdom to Improve Student’s High Order Thinking Skills and Multiple Intelligence on Elementary School

Agusta¹

2021

ijsshr

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The purpose of this study is to produce a new learning model and find out the effectiveness of the learning model GAWI SABUMI to improve the critical thinking, creative thinking, problem-solving, analytical thinking, and logical thinking (high order thinking skills) and multiple intelligences of elementary school students in South Borneo. This study used the type of research and development consisted of three phases to find the effectiveness of the model on the dependent variable through the quasi-experimental research. The research data were analyzed using sequential data analysis to determine the feasibility of the model. The sample was 40 students of Sungai Miai 7 Elementary School Banjarmasin. The result of quality evaluation of the blended learning model GAWI SABUMI was at a high level (X= 4.57) (SD = 0.17). The characteristics of the Gawi Sabumi model included ten stages: Group, Analysis, Work together, Inform, Solve the problem on outdoor, Actualization of the solution, Battle games, Unity on role play, Manage conclusion and Invent the creation. Students had post-test on high order thinking skills average scores higher than pre-test at (X= 3.59) (SD = .78) and students who have highly skilled criteria at linguistic, musical, logical mathematic, visual-spatial, kinesthetic, interpersonal, intrapersonal, naturalist, and existentialist intelligence average scores higher than pre-test at (X= 3.55) (SD = .88). It means that the GAWI SABUMI model meets the criteria of being valid, reliable, and feasible to be implemented and potential to improve students' high order thinking skills and multiple intelligences.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Learning Model Gawi Sabumi Based on Local Wisdom to Improve Student’s High Order Thinking Skills and Multiple Intelligence on Elementary School

Agusta¹

2021

ijsshr

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“…Recently, the ANN models like the Multilayer perceptron model, Radian basis function network model and Generalized regression neural network model were employed for forecasting pressure drop and maximum sustained wind speed of tropical cyclones over the Arabian Sea (Sarkar et al, 2021). The Cooperation search algorithm approach of the extreme learning method model (an AI model) is yet another technique that seems to have a novel multiscale nonstationary hydrological prediction tool (Feng et al, 2021).…”

Section: Overview Of Literaturementioning

confidence: 99%

Long-term forecasting of tropical cyclones over Bay of Bengal using linear and non-linear statistical models

2021

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Forecasting tropical cyclones with climate and physical variability and observed cyclonic disturbances has been developed over the years for all the ocean basins successfully and is still one of the priorities for disaster risk reduction policymaking. This study attempts to forecast seasonal cyclonic disturbances and severe cyclonic storms over the Bay of Bengal, where about 80% of the tropical cyclones of the North Indian Ocean are formed. We have used three time-series models, namely, the seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) model, artificial neural network-nonlinear autoregressive with exogenous variables (ANN-NARX) model, and the hybrid model. The basic purpose of considering three different models is to improve the forecasting accuracy of tropical cyclones. We have shown that the intensification rate of the severe cyclonic storms over the Bay of Bengal has been significant and increasing over the years. Results show that the ANN-NARX model with sea surface temperature and near-surface wind speed as predictors is the best performance model for longterm forecasting of cyclonic disturbances. Hence, the distribution of cyclonic disturbances is non-linear. The correlations between observed and predicted occurrences are 0.80 and 0.85 for cyclonic disturbances and severe cyclonic storms, respectively, corroborating, by and large, the forecasting accuracies of some previous studies. The forecasting of cyclonic disturbances indicates that they will vary from 5 to 13 annually and there will be, on average, one severe cyclonic storm per year. The likelihood of occurrence of severe cyclonic storms is most significant in the post-monsoon season. This forecast till 2050 would help the scientific community and policymakers significantly for applications and good disaster risk governance.

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“…The ELM is a single-layer feedforward neural network with a fast training process. The main pros of this algorithm are high accuracy, robustness, least user intervention, rapid learning rate, a learning process that requires only a single iteration, and high generalization [32]. However, the main drawback of the ELM is the random generation of the two main matrices, including bias of hidden neurons and input weights.…”

Section: Introductionmentioning

confidence: 99%

Prognostication of Shortwave Radiation Using an Improved No-Tuned Fast Machine Learning

et al. 2021

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Shortwave radiation density flux (SRDF) modeling can be key in estimating actual evapotranspiration in plants. SRDF is the result of the specific and scattered reflection of shortwave radiation by the underlying surface. SRDF can have profound effects on some plant biophysical processes such as photosynthesis and land surface energy budgets. Since it is the main energy source for most atmospheric phenomena, SRDF is also widely used in numerical weather forecasting. In the current study, an improved version of the extreme learning machine was developed for SRDF forecasting using the historical value of this variable. To do that, the SRDF through 1981–2019 was extracted by developing JavaScript-based coding in the Google Earth Engine. The most important lags were found using the auto-correlation function and defined fifteen input combinations to model SRDF using the improved extreme learning machine (IELM). The performance of the developed model is evaluated based on the correlation coefficient (R), root mean square error (RMSE), mean absolute percentage error (MAPE), and Nash–Sutcliffe efficiency (NSE). The shortwave radiation was developed for two time ahead forecasting (R = 0.986, RMSE = 21.11, MAPE = 8.68%, NSE = 0.97). Additionally, the estimation uncertainty of the developed improved extreme learning machine is quantified and compared with classical ELM and found to be the least with a value of ±3.64 compared to ±6.9 for the classical extreme learning machine. IELM not only overcomes the limitation of the classical extreme learning machine in random adjusting of bias of hidden neurons and input weights but also provides a simple matrix-based method for practical tasks so that there is no need to have any knowledge of the improved extreme learning machine to use it.

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Evolutionary artificial intelligence model via cooperation search algorithm and extreme learning machine for multiple scales nonstationary hydrological time series prediction

Cited by 54 publications

References 78 publications

Learning Model Gawi Sabumi Based on Local Wisdom to Improve Student’s High Order Thinking Skills and Multiple Intelligence on Elementary School

Learning Model Gawi Sabumi Based on Local Wisdom to Improve Student’s High Order Thinking Skills and Multiple Intelligence on Elementary School

Long-term forecasting of tropical cyclones over Bay of Bengal using linear and non-linear statistical models

Prognostication of Shortwave Radiation Using an Improved No-Tuned Fast Machine Learning

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