scholarly journals Retraction Note to: Neural network-based rainfall estimation in coastal areas and development of students’ English writing

2021 ◽  
Vol 14 (22) ◽  
Author(s):  
Shouxue Ma ◽  
Shaoyan Zhang
Keyword(s):  
Neural Network ◽  
Coastal Areas ◽  
Rainfall Estimation ◽  
English Writing ◽  
Development Of Students

scholarly journals PENERAPAN DIALOGUE JOURNALS PADA PEMBELAJARAN MENULIS BAHASA INGGRIS UNTUK MENINGKATKAN MOTIVASI MENULIS DAN KETERAMPILAN MENULIS SISWA ENGLISH AS ADDITIONAL LANGUAGE (EAL) KELAS 4 DI AUSTRALIAN INDEPENDENT SCHOOL (AIS) [THE IMPLEMENTATION OF DIALOGUE JOURNALS IN ENGLISH WRITING LEARNING TO INCREASE THE WRITING MOTIVATION AND WRITING SKILLS OF GRADE 4 ENGLISH AS ADDITIONAL LANGUAGE (EAL) STUDENTS AT AN AUSTRALIAN INDEPENDENT SCHOOL (AIS)]

2021 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Dwi Endah Puspitowati
Keyword(s):  
Time Management ◽  
Writing Skills ◽  
Independent School ◽  
Implementation Process ◽  
Dialogue Journals ◽  
Observation Study ◽  
Writing Improvement ◽  
English Writing ◽  
Writing Motivation ◽  
Development Of Students

<p>Writing in English takes place intensively but students’ writing motivation was low and their writing improvement didn’t meet the expectation. This situation raised concerns which led to improvement planning that aimed to 1) describe the implementation process of Dialogue Journals, 2) analyze the development of students’ writing motivation, 3) analyze the development of students’ writing skills, and 4) identify obstacles faced during the process of implementation and solutions to overcome them. This research used a classroom action research which consisted of three cycles and was conducted at Australian Independent School with six grade 4 EAL students involved. Resources of data collection were based on class observation, study of documentation, and measurement of students’ writing motivation and writing skills using writing motivation and writing rubric. The results of the research showed that 1) Dialogue Journals were applied based of suggestive steps and plans, 2) 5 out of 6 students’ writing motivation raised , 3) 5 out 6 students’ writing skills improved, 4) obstacles had been identified which included students’ absence, emotional, and behavioural problems which needed immediate solution, and teacher time management to reply to students, and learning tool availability and access. Solutions to the related obstacles were arranged and applied.</p><p><strong>BAHASA INDONESIA ABSTRACT: </strong>Kegiatan menulis bahasa Inggris siswa <em>EAL</em> kelas 4 berlangsung secara intensif namun motivasi menulis siswa rendah dan perkembangan keterampilan menulis siswa tidak sesuai harapan. Kondisi ini merisaukan. Bermula dari kerisauan ini, guru melakukan tindakan perbaikan dengan penerapan <em>Dialogue Journals </em>yang bertujuan: 1) mendeskripsikan proses penerapan <em>Dialogue Journals</em>, 2) menganalisis perkembangan motivasi menulis bahasa Inggris, 3) menganalisis perkembangan keterampilan menulis bahasa Inggris, dan 4) mengidentifikasi kendala yang dihadapi selama proses penerapan berlangsung dan solusinya. Penelitian ini menggunakan Penelitian Tindakan Kelas (PTK). Penelitian dilaksanakan dalam tiga siklus di <em>Australian Independent School</em> (<em>AIS</em>). Subyek penelitian terdiri dari enam orang siswa dan siswi kelas 4. Pengumpulan data dilakukan melalui observasi, studi dokumentasi dan penilaian dengan menggunakan rubrik, serta dianalisis secara kualitatif deskriptif. Hasil penelitian menunjukkan: 1) penerapan <em>Dialogue Journals </em>sesuai dengan langkah-langkah yang disarankan dan direncanakan, 2) motivasi menulis 5 dari 6 orang siswa meningkat, 3) keterampilan menulis 5 dari 6 siswa meningkat dan 4) kendala dalam proses penerapan diantaranya ketidakhadiran siswa, masalah sikap yang timbul yang perlu penanganan segera, manajemen waktu guru,  penggunaan  dan ketersediaan fasilitas alat bantu. Solusi berupa penyediaan strategi manajemen emosi dan sikap, mengatur waktu dan mendorong siswa belajar secara mandiri.</p>

Rainfall Estimation Using Neuron-Adaptive Higher Order Neural Networks

2010 ◽  
pp. 159-186 ◽  
Author(s):  
Ming Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
High Frequency ◽  
Higher Order ◽  
Rainfall Estimation ◽  
Test Error ◽  
Artificial Neural ◽  
Training Error ◽  
Optimum Model ◽  
Artificial Neural Network Ann

Real world data is often nonlinear, discontinuous and may comprise high frequency, multi-polynomial components. Not surprisingly, it is hard to find the best models for modeling such data. Classical neural network models are unable to automatically determine the optimum model and appropriate order for data approximation. In order to solve this problem, Neuron-Adaptive Higher Order Neural Network (NAHONN) Models have been introduced. Definitions of one-dimensional, two-dimensional, and n-dimensional NAHONN models are studied. Specialized NAHONN models are also described. NAHONN models are shown to be “open box”. These models are further shown to be capable of automatically finding not only the optimum model but also the appropriate order for high frequency, multi-polynomial, discontinuous data. Rainfall estimation experimental results confirm model convergence. We further demonstrate that NAHONN models are capable of modeling satellite data. When the Xie and Scofield (1989) technique was used, the average error of the operator-computed IFFA rainfall estimates was 30.41%. For the Artificial Neural Network (ANN) reasoning network, the training error was 6.55% and the test error 16.91%, respectively. When the neural network group was used on these same fifteen cases, the average training error of rainfall estimation was 1.43%, and the average test error of rainfall estimation was 3.89%. When the neuron-adaptive artificial neural network group models was used on these same fifteen cases, the average training error of rainfall estimation was 1.31%, and the average test error of rainfall estimation was 3.40%. When the artificial neuron-adaptive higher order neural network model was used on these same fifteen cases, the average training error of rainfall estimation was 1.20%, and the average test error of rainfall estimation was 3.12%.

scholarly journals Investigating rainfall estimation from radar measurements using neural networks

2013 ◽  
Vol 13 (3) ◽  
pp. 535-544 ◽  
Author(s):  
A. Alqudah ◽  
V. Chandrasekar ◽  
M. Le
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Rain Gauge ◽  
Training Data ◽  
Training Dataset ◽  
Dimensional Structure ◽  
Rbf Neural Networks ◽  
Rainfall Estimation ◽  
Training Time ◽  
Radar Measurements

Abstract. Rainfall observed on the ground is dependent on the four dimensional structure of precipitation aloft. Scanning radars can observe the four dimensional structure of precipitation. Neural network is a nonparametric method to represent the nonlinear relationship between radar measurements and rainfall rate. The relationship is derived directly from a dataset consisting of radar measurements and rain gauge measurements. The performance of neural network based rainfall estimation is subject to many factors, such as the representativeness and sufficiency of the training dataset, the generalization capability of the network to new data, seasonal changes, and regional changes. Improving the performance of the neural network for real time applications is of great interest. The goal of this paper is to investigate the performance of rainfall estimation based on Radial Basis Function (RBF) neural networks using radar reflectivity as input and rain gauge as the target. Data from Melbourne, Florida NEXRAD (Next Generation Weather Radar) ground radar (KMLB) over different years along with rain gauge measurements are used to conduct various investigations related to this problem. A direct gauge comparison study is done to demonstrate the improvement brought in by the neural networks and to show the feasibility of this system. The principal components analysis (PCA) technique is also used to reduce the dimensionality of the training dataset. Reducing the dimensionality of the input training data will reduce the training time as well as reduce the network complexity which will also avoid over fitting.

scholarly journals Temporally Generalizable Land Cover Classification: A Recurrent Convolutional Neural Network Unveils Major Coastal Change through Time

2021 ◽  
Vol 13 (19) ◽  
pp. 3953
Author(s):  
Patrick Clifton Gray ◽  
Diego F. Chamorro ◽  
Justin T. Ridge ◽  
Hannah Rae Kerner ◽  
Emily A. Ury ◽  
...  
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Deep Learning ◽  
Land Cover ◽  
Convolutional Neural Network ◽  
Agricultural Land ◽  
Coastal Areas ◽  
Support Vector ◽  
Validation Data

The ability to accurately classify land cover in periods before appropriate training and validation data exist is a critical step towards understanding subtle long-term impacts of climate change. These trends cannot be properly understood and distinguished from individual disturbance events or decadal cycles using only a decade or less of data. Understanding these long-term changes in low lying coastal areas, home to a huge proportion of the global population, is of particular importance. Relatively simple deep learning models that extract representative spatiotemporal patterns can lead to major improvements in temporal generalizability. To provide insight into major changes in low lying coastal areas, our study (1) developed a recurrent convolutional neural network that incorporates spectral, spatial, and temporal contexts for predicting land cover class, (2) evaluated this model across time and space and compared this model to conventional Random Forest and Support Vector Machine methods as well as other deep learning approaches, and (3) applied this model to classify land cover across 20 years of Landsat 5 data in the low-lying coastal plain of North Carolina, USA. We observed striking changes related to sea level rise that support evidence on a smaller scale of agricultural land and forests transitioning into wetlands and “ghost forests”. This work demonstrates that recurrent convolutional neural networks should be considered when a model is needed that can generalize across time and that they can help uncover important trends necessary for understanding and responding to climate change in vulnerable coastal regions.

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