Development of ARIMA technique in determining the ocean climate prediction skills for pre-service teacher

Rosmiati, R.; Liliasari, S.; Tjasyono, Bayong; Ramalis, Taufik Ramlan

doi:10.1088/1742-6596/1731/1/012072

Cited by 14 publications

(14 citation statements)

References 16 publications

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“…On the indicators of argument analysis: students have been able to identify the important parts of the argument and provide correct statements from the given data set [31,33]. On indicators of probability and uncertainty analysis: students have been able to understand the probability and likelihood of an event occurring and make valid predictions [28,[34][35][36]. On indicators of problemsolving and decision making: students have been able to identify the best among several alternatives in solving problems, evaluating solutions to problems, and making solid and evidence-based decisions [37,38].…”

Section: Figure 1 Results Of Cts Process Performance Assessmentmentioning

confidence: 99%

Improving Critical Thinking Skills (CTS) of Students Through Wave Energy Learning Project (WELP) on Environmental Physics Lecture

Satriawan

Rosmiati

Saputra

et al. 2022

J. Phys.: Conf. Ser.

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This study aims to encourage the critical thinking skills (CTS) of students through the Wave Energy Learning Project (WELP) program, which is applied with an early-assisted project-based learning model (EA-PjBL) in Environmental Physics lectures. The research focuses on the topic of the environment as a renewable energy source, especially ocean wave energy. In the WELP program, there is a prototype of ocean wave energy that has been developed as a teaching aid and an initial project model that pre-service physics teachers will develop. The data collection instrument used was a description test of the concept of ocean wave energy based on CTS, performance and product assessments, peer performance assessments, lecture observation sheets, and pre-service physics teachers’ perception sheets. The research subjects were 31 pre-service physics teachers in the sixth semester of the Physics Education study program at one of the academic staff education institutions in West Nusa Tenggara. The research data were analyzed using descriptive statistics, N-gain test, t-test, and effect size analysis. The data analysis results show a strong influence on the use of the WELP program through the EA-PjBL learning model on improving the CTS of pre-service physics teachers. The CTS of pre-service teachers has increased by category after attending lectures using the WELP program. This research implies that the WELP program developed can be a model for developing lecture programs on other renewable energy materials that enhance the development of higher-order thinking skills. The project in this research triggers the younger generation to contribute to overcoming the global electrical energy crisis.

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Section: Figure 1 Results Of Cts Process Performance Assessmentmentioning

confidence: 99%

Improving Critical Thinking Skills (CTS) of Students Through Wave Energy Learning Project (WELP) on Environmental Physics Lecture

Satriawan

Rosmiati

Saputra

et al. 2022

J. Phys.: Conf. Ser.

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“…Their results showed the ARIMA models to be the best models, with reasonable accuracy to forecast droughts with up to 12 months of lead time. Other studies have applied ARIMA to predict ENSO (e.g., Rosmiati et al., 2021 ), AMO (e.g., Frajka‐Williams et al., 2017 ), and PDO (e.g., Diodato et al., 2019 ).…”

Section: Predicting the Global Climate Indicesmentioning

confidence: 99%

A Place‐Based Approach to Drought Forecasting in South‐Central Oklahoma

McPherson

Corporal‐Lodangco

Richman

2022

Earth and Space Science

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To assist water managers in south‐central Oklahoma prepare for future drought, reliable place‐based drought forecasts are produced. Past‐, present‐, and future‐forecasted climate indices (Multivariate ENSO Index, Pacific Decadal Oscillation index, and Atlantic Multidecadal Oscillation index) and past and present Palmer Drought Severity Index (PDSI) are employed as predictor variables to forecast PDSI using a multivariate regression technique. PDSI is forecasted 18 months in advance with sufficient skill to provide water managers early warning of drought. Using a training data set obtained from the period January 1901 to November 2021, a second‐order model equation that contains, without any restriction, all the predictors and their interaction terms is built to predict drought intensity. Significant predictors are selected through stepwise regression, with cross‐validation producing the simplest restricted model that describes the data well. PDSI values are predicted using 1000 fitted restricted models produced from bootstrapping, then averaged monthly. The technique found the best‐fitting model and estimated the model coefficients that minimized the sum of squared deviations between the fitted model and the predictor variables. The adjusted R‐squared value of the restricted model is large enough to explain an adequately accurate model, and relatively low values of error measures point to good predictive ability of the model. Although the model slightly overestimates the PDSI forecast maxima and minima, it necessarily captures the timing of the periods of severe to exceptional drought.

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“…Statistical forecasting methods, when the amount of data in the sample is large, can establish a forecast model in the statistical sense without considering the physical laws of the forecast object [7]. Statistical forecasting methods used for SSTA forecasting include support vector machines (SVM) [8], autoregressive integrated moving average (ARIMA) [9], regression model [10], and neural networks [11][12][13]. However, the nonlinear and non-stationary data reduces prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Multistep-Ahead Prediction of Ocean SSTA Based on Hybrid Empirical Mode Decomposition and Gated Recurrent Unit Model

Liu

Guo

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The prediction of sea surface temperature anomalies (SSTA) is vital to the study of marine ecosystems and global climate. The SSTA can be accurately forecasted one step ahead by numerical and statistical methods. However, multi-step-ahead forecasting for SSTA is greatly challenging since the nonlinearity and non-stationarity of SSTA and the lag problem of prediction. Therefore, in this paper, a multi-step-ahead SSTA forecasting method based on the hybrid empirical mode decomposition (EMD) and gated recurrent unit (GRU) model is proposed considering that EMD has the advantage of reducing data complexity and GRU is good at long-term prediction of data. First, the EMD algorithm is applied to obtain several intrinsic modal mode functions (IMFs) that are more stationary than the original data. Then GRU is used for multi-step forecasting, in which three multistep forecasting strategies (recursive, direct, and multi-output) are compared. The proposed hybrid model is validated by multi-step forecasting for monthly average SSTA at the Niño3.4 region. The experimental results show that using reconstruction error as part of the prediction effectively improves the prediction accuracy of the EEMD-GRU model and outperforms other EMD algorithms combined with GRU. Compared with traditional models, the EEMD-GRU model can better predict future multi-month trends of SSTA and effectively alleviate the problem of prediction lag of the traditional model. Finally, this model is applied to the Niño3.4 regional SSTA prediction, and the results show that this model can provide a reference for ocean research.

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Development of ARIMA technique in determining the ocean climate prediction skills for pre-service teacher

Cited by 14 publications

References 16 publications

Improving Critical Thinking Skills (CTS) of Students Through Wave Energy Learning Project (WELP) on Environmental Physics Lecture

Improving Critical Thinking Skills (CTS) of Students Through Wave Energy Learning Project (WELP) on Environmental Physics Lecture

A Place‐Based Approach to Drought Forecasting in South‐Central Oklahoma

Multistep-Ahead Prediction of Ocean SSTA Based on Hybrid Empirical Mode Decomposition and Gated Recurrent Unit Model

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