Prediction of Significant Wave Height Using Neural Network in the Java Sea (North of Surabaya)

Indonesia is an archipelago, Surabaya is the second crowded city in Indonesia. So the shipping lane and the city is comparable. Neural network is models inspired by biological neural networks and used to estimate or approximate functions that can depend on a large number of inputs and are generally unknown. Neural network is used to predict the wave height in Java Sea (The North of Surabaya). The Root Mean Square Error average for the next one hour is 0.03 and the Root Mean Square Error average for the next six hours is 0.09. That’s mean the longest the prediction, the biggest Root Mean Square error.

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Comparison of neural networks and genetic algorithms to determine missing precipitation data (Case study: the city of Sari)

Revista de la Universidad del Zulia ◽

10.46925//rdluz.29.08 ◽

2020 ◽

Vol 11 (29) ◽

pp. 114-128

Author(s):

Ali Mahdavi ◽

Mohsen Najarchi ◽

Emadoddin Hazaveie ◽

Seyed Mohammad Mirhosayni Hazave ◽

Seyed Mohammad Mahdai Najafizadeh

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Genetic Programming ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Correlation Coefficients ◽

Mean Square ◽

Artificial Neural ◽

The City

Neural networks and genetic programming in the investigation of new methods for predicting rainfall in the catchment area of the city of Sari. Various methods are used for prediction, such as the time series model, artificial neural networks, fuzzy logic, fuzzy Nero, and genetic programming. Results based on statistical indicators of root mean square error and correlation coefficient were studied. The results of the optimal model of genetic programming were compared, the correlation coefficients and the root mean square error 0.973 and 0.034 respectively for training, and 0.964 and 0.057 respectively for the optimal neural network model. Genetic programming has been more accurate than artificial neural networks and is recommended as a good way to accurately predict.

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Subset selection of markers for the genome-enabled prediction of genetic values using radial basis function neural networks

Acta Scientiarum Agronomy ◽

10.4025/actasciagron.v43i1.46307 ◽

2020 ◽

Vol 43 ◽

pp. e46307 ◽

Cited By ~ 1

Author(s):

Isabela de Castro Sant'Anna ◽

Gabi Nunes Silva ◽

Moysés Nascimento ◽

Cosme Damião Cruz

Keyword(s):

Neural Networks ◽

Root Mean Square Error ◽

Radial Basis Function ◽

Mean Square Error ◽

Root Mean Square ◽

Basis Function ◽

Stepwise Regression ◽

Subset Selection ◽

Mean Square ◽

Selection Of

This paper aimed to evaluate the effectiveness of subset selection of markers for genome-enabled prediction of genetic values using radial basis function neural networks (RBFNN). To this end, an F1 population derived from the hybridization of divergent parents with 500 individuals genotyped with 1000 SNP-type markers was simulated. Phenotypic traits were determined by adopting three different gene action models – additive, additive-dominant, and epistatic, representing two dominance situations: partial and complete with quantitative traits having a heritability (h2) of 30 and 60%; traits were controlled by 50 loci, considering two alleles per locus. Twelve different scenarios were represented in the simulation. The stepwise regression was used before the prediction methods. The reliability and the root mean square error were used for estimation using a fivefold cross-validation scheme. Overall, dimensionality reduction improved the reliability values for all scenarios, specifically with h2 =30 the reliability value from 0.03 to 0.59 using RBFNN and from 0.10 to 0.57 with RR-BLUP in the scenario with additive effects. In the additive dominant scenario, the reliability values changed from 0.12 to 0.59 using RBFNN and from 0.12 to 0.58 with RR-BLUP, and in the epistasis scenarios, the reliability values changed from 0.07 to 0.50 using RBFNN and from 0.06 to 0.47 with RR-BLUP. The results showed that the use of stepwise regression before the use of these techniques led to an improvement in the accuracy of prediction of the genetic value and, mainly, to a large reduction of the root mean square error in addition to facilitating processing and analysis time due to a reduction in dimensionality.

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Evaluation of Sentinel-3A Wave Height Observations Near the Coast of Southwest England

Remote Sensing ◽

10.3390/rs11242998 ◽

2019 ◽

Vol 11 (24) ◽

pp. 2998 ◽

Cited By ~ 8

Author(s):

Francesco Nencioli ◽

Graham D. Quartly

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Wave Height ◽

Significant Wave Height ◽

Mean Square ◽

Significant Wave ◽

In Situ Observations ◽

Regression Slopes

Due to the smaller ground footprint and higher spatial resolution of the Synthetic Aperture Radar (SAR) mode, altimeter observations from the Sentinel-3 satellites are expected to be overall more accurate in coastal areas than conventional nadir altimetry. The performance of Sentinel-3A in the coastal region of southwest England was assessed by comparing SAR mode observations of significant wave height against those of Pseudo Low Resolution Mode (PLRM). Sentinel-3A observations were evaluated against in-situ observations from a network of 17 coastal wave buoys, which provided continuous time-series of hourly values of significant wave height, period and direction. As the buoys are evenly distributed along the coast of southwest England, they are representative of a broad range of morphological configurations and swell conditions against which to assess Sentinel-3 SAR observations. The analysis indicates that SAR observations outperform PLRM within 15 km from the coast. Within that region, regression slopes between SAR and buoy observations are close to the 1:1 relation, and the average root mean square error between the two is 0.46 ± 0.14 m. On the other hand, regression slopes for PLRM observations rapidly deviate from the 1:1 relation, while the average root mean square error increases to 0.84 ± 0.45 m. The analysis did not identify any dependence of the bias between SAR and in-situ observation on the swell period or direction. The validation is based on a synergistic approach which combines satellite and in-situ observations with innovative use of numerical wave model output to help inform the choice of comparison regions. Such an approach could be successfully applied in future studies to assess the performance of SAR observations over other combinations of coastal regions and altimeters.

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Prediction of Lower Extremity Multi-Joint Angles during Overground Walking by Using a Single IMU with a Low Frequency Based on an LSTM Recurrent Neural Network

Sensors ◽

10.3390/s22010053 ◽

2021 ◽

Vol 22 (1) ◽

pp. 53

Author(s):

Joohwan Sung ◽

Sungmin Han ◽

Heesu Park ◽

Hyun-Myung Cho ◽

Soree Hwang ◽

...

Keyword(s):

Neural Network ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Recurrent Neural Network ◽

Joint Angle ◽

Low Frequency ◽

Measurement Unit ◽

Mean Square ◽

Overground Walking

The joint angle during gait is an important indicator, such as injury risk index, rehabilitation status evaluation, etc. To analyze gait, inertial measurement unit (IMU) sensors have been used in studies and continuously developed; however, they are difficult to utilize in daily life because of the inconvenience of having to attach multiple sensors together and the difficulty of long-term use due to the battery consumption required for high data sampling rates. To overcome these problems, this study propose a multi-joint angle estimation method based on a long short-term memory (LSTM) recurrent neural network with a single low-frequency (23 Hz) IMU sensor. IMU sensor data attached to the lateral shank were measured during overground walking at a self-selected speed for 30 healthy young persons. The results show a comparatively good accuracy level, similar to previous studies using high-frequency IMU sensors. Compared to the reference results obtained from the motion capture system, the estimated angle coefficient of determination (R2) is greater than 0.74, and the root mean square error and normalized root mean square error (NRMSE) are less than 7° and 9.87%, respectively. The knee joint showed the best estimation performance in terms of the NRMSE and R2 among the hip, knee, and ankle joints.

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Penerapan Generalized Regression Neural Networks untuk Memprediksi Produksi Padi Terhadap Perubahan Iklim

Jurnal Teknologi Rekayasa ◽

10.31544/jtera.v2.i2.2017.117-124 ◽

2017 ◽

Vol 2 (2) ◽

pp. 117 ◽

Cited By ~ 1

Author(s):

Muhammad Alkaff ◽

Yuslena Sari

Keyword(s):

Neural Networks ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Input Data ◽

Mean Square ◽

Generalized Regression Neural Networks ◽

Data Output ◽

Generalized Regression

Padi sebagai bahan makanan pokok utama bagi masyarakat Indonesia merupakan tanaman pangan yang rentan terhadap perubahan iklim. Pendataan dan perhitungan ramalan hasil produksi padi sangat diperlukan untuk mendukung kebijakan yang berkaitan dengan ketahanan pangan. Penelitian ini bertujuan untuk melakukan peramalan terhadap produksi padi di Kabupaten Barito Kuala sebagai kabupaten penghasil padi terbesar di Kalimantan Selatan dengan menggunakan data iklim sebagai input. Data iklim yang digunakan berasal dari Stasiun Meteorologi Syamsudin Noor, sedangkan sebagai data output adalah data produksi padi dari Badan Pusat Statistika (BPS) Provinsi Kalimantan Selatan. Metode yang digunakan untuk melakukan peramalan produksi padi adalah Generalized Regression Neural Networks (GRNN). Dari hasil pengujian didapatkan nilai Root Mean Square Error (RMSE) sebesar 0,296 dengan menggunakan parameter smoothness bernilai 1.Kata kunci: padi, iklim, Barito Kuala, GRNN, RMSE

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Breaking Wave Height Estimation from Timex Images: Two Methods for Coastal Video Monitoring Systems

Remote Sensing ◽

10.3390/rs12020204 ◽

2020 ◽

Vol 12 (2) ◽

pp. 204 ◽

Cited By ~ 5

Author(s):

Umberto Andriolo ◽

Diogo Mendes ◽

Rui Taborda

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Wave Height ◽

Video Data ◽

Video Monitoring ◽

Monitoring Systems ◽

Breaking Wave ◽

Mean Square ◽

Beach Profile

The breaking wave height is a crucial parameter for coastal studies but direct measurements constitute a difficult task due to logistical and technical constraints. This paper presents two new practical methods for estimating the breaking wave height from digital images collected by shore-based video monitoring systems. Both methods use time-exposure (Timex) images and exploit the cross-shore length ( L H s ) of the typical time-averaged signature of breaking wave foam. The first method ( H s b , v ) combines L H s and a series of video-derived parameters with the beach profile elevation to obtain the breaking wave height through an empirical formulation. The second method ( H s b , v 24 ) is based on the empirical finding that L H s can be associated with the local water depth at breaking, thus it can be used to estimate the breaking wave height without the requirement of local bathymetry. Both methods were applied and verified against field data collected at the Portuguese Atlantic coast over two days using video acquired by an online-streaming surfcam. Furthermore, H s b , v 24 was applied on coastal images acquired at four additional field sites during distinct hydrodynamic conditions, and the results were compared to a series of different wave sources. Achievements suggest that H s b , v method represents a good alternative to numerical hydrodynamic modeling when local bathymetry is available. In fact, the differences against modeled breaking wave height, ranging from 1 to 3 m at the case study, returned a root-mean-square-error of 0.2 m. The H s b , v 24 method, when applied on video data collected at five sites, assessed a normalized root-mean-square-error of 18% on average, for dataset of about 900 records and breaking wave height ranging between 0.1 and 3.8 m. These differences demonstrate the potential of H s b , v 24 in estimating breaking wave height merely using Timex images, with the main advantage of not requiring the beach profile. Both methods can be easily implemented as cost-effective tools for hydrodynamic applications in the operational coastal video systems worldwide. In addition, the methods have the potential to be coupled to the numerous other Timex applications for morphodynamic studies.

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Hydrochars as Emerging Biofuels: Recent Advances and Application of Artificial Neural Networks for the Prediction of Heating Values

Energies ◽

10.3390/en13174572 ◽

2020 ◽

Vol 13 (17) ◽

pp. 4572

Author(s):

Ioannis O. Vardiambasis ◽

Theodoros N. Kapetanakis ◽

Christos D. Nikolopoulos ◽

Trinh Kieu Trang ◽

Toshiki Tsubota ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Sewage Sludge ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Food Waste ◽

Hydrothermal Carbonization ◽

Mean Square ◽

Artificial Neural

In this study, the growing scientific field of alternative biofuels was examined, with respect to hydrochars produced from renewable biomasses. Hydrochars are the solid products of hydrothermal carbonization (HTC) and their properties depend on the initial biomass and the temperature and duration of treatment. The basic (Scopus) and advanced (Citespace) analysis of literature showed that this is a dynamic research area, with several sub-fields of intense activity. The focus of researchers on sewage sludge and food waste as hydrochar precursors was highlighted and reviewed. It was established that hydrochars have improved behavior as fuels compared to these feedstocks. Food waste can be particularly useful in co-hydrothermal carbonization with ash-rich materials. In the case of sewage sludge, simultaneous P recovery from the HTC wastewater may add more value to the process. For both feedstocks, results from large-scale HTC are practically non-existent. Following the review, related data from the years 2014–2020 were retrieved and fitted into four different artificial neural networks (ANNs). Based on the elemental content, HTC temperature and time (as inputs), the higher heating values (HHVs) and yields (as outputs) could be successfully predicted, regardless of original biomass used for hydrochar production. ANN3 (based on C, O, H content, and HTC temperature) showed the optimum HHV predicting performance (R2 0.917, root mean square error 1.124), however, hydrochars’ HHVs could also be satisfactorily predicted by the C content alone (ANN1, R2 0.897, root mean square error 1.289).

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IMPLEMENTASI METODE NEURAL NETWORK BACKPROPAGATION DALAM PREDIKSI INDEKS HARGA SAHAM GABUNGAN (IHSG)

SEMINASTIKA ◽

10.47002/seminastika.v3i1.252 ◽

2021 ◽

Vol 3 (1) ◽

pp. 79-85

Author(s):

Okky Barus ◽

Christopher Wijaya

Keyword(s):

Neural Network ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Composite Index ◽

Mean Square

Pada era saat ini, Investasi saham di pasar modal merupakan aset yang sangat penting bagi beberapa golongan masyarakat dan juga bagi perusahaan. Dengan adanya investasi, secara langsung maupun tidak langsung dapat memberikan dampak bagi perusahaan maupun bagi masyarakat. Penelitian ini bertujuan untuk memprediksi Indeks Harga Saham Gabungan (IHSG) dengan indeks saham: Jakarta Composite Index (JKSE). Metode yang digunakan pada penelitian ini adalah Neural Network Backpropagation. Pengumpulan dataset melalui website finance.yahoo.com dengan periode 8 Mei 2018 sampai dengan 7 Mei 2021 sebanyak 757 data. Setelah melakukan proses pengolahan data, data yang tersisa adalah 724 data. Kemudian data akan dibagi menjadi 70% data training dan 30% data testing yang akan digunakan pada proses pengolahan data. Hasil pengujian menggunakan metode Neural Newtwork Backpropagation mendapatkan hasil terbaik menggunakan Kondisi ke-10 dengan nilai Root Mean Square Error (RMSE) senilai 0.010. Kemudian akan didapatkan hasil perbandingan antara harga Close aktual dengan harga Close prediksi dengan akurasi sebesar 63.06% yang dapat membantu dalam pengambilan keputusan para investor.

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Prediction of quality attributes and maturity classification of pear fruit using laser imaging and Artificial Neural Network

Food Research ◽

10.26656/fr.2017.5(s1).022 ◽

2021 ◽

Vol 5 (S1) ◽

pp. 144-151

Author(s):

S.E. Adebayo ◽

N. Hashim

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Optical Properties ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Mean Square ◽

Classification Models ◽

Pear Fruit ◽

Laser Imaging

In this study, the application of laser imaging technique was utilized to predict the quality attributes (firmness and soluble solids content) of pear fruit and to classify the maturity stages of the fruit harvested at different days after full bloom (dafb). Laser imaging system emitting at visible and near infra-red region (532, 660, 785, 830 and 1060 nm) was deployed to capture the images of the fruit. Optical properties (absorption ma and reduced scattering ms ʹ coefficients) at individual and combined wavelengths of the laser images of the fruit were used in the prediction and classifications of the maturity stages. Artificial neural network (ANN) was employed in the building of both prediction and classification models. Root mean square error of calibration (RMSEC), root mean square error of crossvalidation (RMSECV), correlation coefficient (r) and bias were used to test the performance of the prediction models while sensitivity and specificity were used to evaluate the classification models. The results showed that there was a very strong correlation between the ma and ms ʹ with pear development. This study had shown that optical properties of pears with ANN as prediction and classification models can be employed to both predict quality parameters of pear and classify pear into different (dafb) non-destructively.

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Prediksi Permintaan Kargo pada Cargo Service Center Tangerang City Menggunakan Metode Gated Recurrent Unit

Repositor ◽

10.22219/repositor.v2i8.972 ◽

2020 ◽

Vol 2 (8) ◽

Author(s):

Rifky Ahmad Saputra

Keyword(s):

Neural Network ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Recurrent Neural Network ◽

Mean Square ◽

Service Center ◽

Gated Recurrent Unit

Pada saat ini persaingan bisnis dalam bidang layanan kargo khususnya di Indonesia semakin ketat. Terdapat beberapa perusahaan layanan kargo di Indonesia, salah satunya yaitu Cargo Service Center Tangerang City. Untuk mengantisipasi persaingan bisnis tersebut, Cargo Service Center Tangerang City harus dapat menentukan strategi manajemen usaha, baik dalam jangka menengah maupun jangka panjang. Salah satunya hal yang dapat dilakukan yaitu prediksi permintaan kargo. Pada Cargo Service Center Tangerang City terdapat data transaksi kargo mulai dari Januari 2016 hingga Septermber 2019, oleh karena itu dilakukanlah penelitian yaitu mengimplementasikan metode Gated Recurrent Unit untuk melakukan prediksi permintaan kargo. metode Gated Recurrent Unit merupakan model pengembangan dari Recurrent Neural Network yang biasa digunakan untuk melakukan prediksi pada data sekuens. Pengujian model prediksi dalam penelitian ini dilakukan dengan mencari nilai Root Mean Square Error terkecil dari beberapa percobaan. Hasil dari penelitian ini menunjukkan bahwa model cukup baik dalam melakukan prediksi permintaan kargo, namun terdapat beberapa hasil prediksi metode Gated Recurrent Unit yang masih belum maksimal mendekati nilai aktual misalnya pada nilai aktual yang berada di titik puncak.

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