Deep Learning Stream Temperature Model: Recommendations for Modeling Gauged and Ungauged Basins

2021 ◽  
Author(s):  
Farshid Rahmani ◽  
Kathryn Lawson ◽  
Samantha Oliver ◽  
Alison Appling ◽  
Chaopeng Shen
Keyword(s):  
Deep Learning ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
The United States ◽  
Data Availability ◽  
Efficiency Coefficient ◽  
Mean Square ◽  
Training Set ◽  
The Impact

<p>Stream water temperature (T<sub>s</sub>) is a variable that plays a pivotal role in managing water resources. We used the long short-term memory (LSTM) deep learning architecture to develop a basin centric single T<sub>s</sub> model based on general meteorological data and basin meteo-geological attributes. We created a strong tool for long-term Ts projection and subsequently, improved the Ts model using novel approaches. We investigated the impact of both observed and simulated streamflow data on improving the model accuracy. At a national scale, we obtained a median root-mean-square error (RMSE) of 0.69 <sup>o</sup>C, and Nash-Sutcliffe model efficiency coefficient (NSE) of 0.985, which are marked improvements over previous values reported in previous studies. In order to test the performance of the model on basins ranging from basins with extensive data to unmonitored basins, we used more than 400 basins with different data-availability groups (DAG) across the continent of the United States to explore how to assemble the training dataset for both monitored and unmonitored basins. Best root-mean-square error (RMSE) for sites with extensive (99%), intermediate (60%), scarce (10%) and absent (0%) data for training were 0.75, 0.837, 0.889, and 1.595 <sup>o</sup>C, respectively. We observed the negative effect of the presence of reservoirs in T<sub>s</sub> modeling. Our results illustrated that the most suitable training set should be different in modeling basins with different availability of observed data. for predicting T<sub>s</sub> in a monitored basin, including basins that have at least equal DAG with that particular basin will result in most accurate predictions, however, for T<sub>s</sub> prediction in ungauged basin, including all basins in training section will generate the best model, showing a more diverse training set. Furthermore, to decrease overfitting produced by attributes for PUB application, we could improve the accuracy of the model using input-selection ensemble method. We got median correlation higher than 0.90 for PUB after seasonality was removed which is still high. While many T<sub>s</sub> prediction models showed better performance in summer, our model was on the opposite side. We found a strong relationship between general available daily meteorological variables and catchment attributes with the presented T<sub>s</sub> model. However, our results indicate that combining physics-based criteria to the model can improve the prediction of temperature in river networks.</p><p>.</p>

Reliability and accuracy of helmet-mounted and head-mounted devices used to measure head accelerations

2016 ◽  
Vol 231 (2) ◽  
pp. 144-153 ◽  
Author(s):  
Brian Cummiskey ◽  
David Schiffmiller ◽  
Thomas M Talavage ◽  
Larry Leverenz ◽  
Janette J Meyer ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mean Square ◽  
High School Football ◽  
Head Impacts ◽  
Football Team ◽  
Hybrid Iii ◽  
Future Work ◽  
The Impact

The attention given to brain injury has grown in recent years as its effects have become better understood. A desire to investigate the causal agents of head trauma in athletes has led to the development and use of several devices that track head impacts. In order to determine which devices best measure these impacts, a Hybrid III headform was used to quantify the accuracy for translational and angular accelerations. Testing was performed by mounting each device into the helmet as instructed by its manufacturer, fitting the helmet on the headform, and impacting the helmet using an impulse hammer. The root mean square error for the peak translational acceleration varied with location. The worst root mean square error for a head-mounted device was 74.7% while the worst for a helmet-mounted device was 298%. Head-mounted devices consistently outperformed those mounted in helmets, suggesting that future sensor designs should avoid attachment to the helmet. Deployment to a high school football team affirmed differences between two of the device models, but strongly indicated that head-mounted systems require further development to account for variation between individuals, the relative motion of the skin, and helmet–sensor interactions. Future work needs to account for these issues, refine the algorithms used to estimate the translational and angular accelerations, and examine technologies that better locate the source of the impact.

The Better Predictive Model: High q2 for the Training Set or Low Root Mean Square Error of Prediction for the Test Set?

2005 ◽  
Vol 24 (3) ◽  
pp. 385-396 ◽  
Author(s):  
Aynur O. Aptula ◽  
Nina G. Jeliazkova ◽  
Terry W. Schultz ◽  
Mark T. D. Cronin
Keyword(s):  
Root Mean Square Error ◽  
Predictive Model ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mean Square ◽  
Training Set ◽  
Test Set

Analysis of sorghum content in corn–sorghum flour bioethanol feedstock by near infrared spectroscopy

2020 ◽  
Vol 28 (5-6) ◽  
pp. 267-274
Author(s):  
KHS Peiris ◽  
SR Bean ◽  
M Tilley ◽  
SVK Jagadish
Keyword(s):  
Infrared Spectroscopy ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
The United States ◽  
Calibration Model ◽  
Mean Square ◽  
Sorghum Flour

In the sorghum-growing regions of the United States, some bioethanol plants use mixtures of corn and sorghum grains as feedstocks depending on price and availability. For regulatory purposes and for optimizing the ethanol manufacturing process, knowledge of the grain composition of the milled feedstock is important. Thus, a near infrared spectroscopy method was developed to determine the content of sorghum in corn–sorghum flour mixtures. Commercial corn and sorghum grain samples were obtained from a bioethanol plant over an 18-month period and across two crop seasons. An array of corn–sorghum flour mixtures having 0–100% sorghum was prepared and scanned using a near infrared spectrometer in the 950–1650 nm wavelength range. A partial least squares regression model was developed to estimate sorghum content in flour mixtures. A calibration model with R2 of 0.99 and a root mean square error of cross validation of 3.91% predicted the sorghum content of an independent set of flour mixtures with r2 = 0.97, root mean square error of prediction = 5.25% and bias = −0.49%. Fourier-transform infrared spectroscopy was utilized to examine spectral differences in corn and sorghum flours. Differences in absorptions were observed at 2930, 2860, 1710, 1150, 1078, and 988 cm−1 suggesting that C–H antisymmetric and symmetric, C=O and C–O stretch vibrations of corn and sorghum flours differ. The regression coefficients of the near infrared model had major peaks around overtone and combination bands of C–H stretch and bending vibrations at 1165, 1220, and 1350 nm. Therefore, the above results confirmed that sorghum content in corn sorghum flour mixtures can be determined using near infrared spectroscopy.

PERAMALAN BEBAN JANGKA PENDEK SISTEM KELISTRIKAN KOTA BATU MENGGUNAKAN DEEP LEARNING LONG SHORT-TERM MEMORY

Transmisi ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 97-102
Author(s):  
Heru Purnomo ◽  
Hadi Suyono ◽  
Rini Nur Hasanah
Keyword(s):  
Deep Learning ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Short Term Memory ◽  
Mean Square ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Dalam rangka proyeksi kebutuhan listrik dimasa mendatang, maka penyedia listrik dapat melakukan peramalan terkait besarnya kebutuhan dan permintaan energi listrik. Apabila besarnya permintaan listrik tidak dilakukan peramalan, maka akan terjadi kelebihan kapasitas yang menyebabkan tidak terserapnya sumber energi yang tersedia. Berdasarkan hasil penelitian diperoleh kesimpulan bahwa model terbaik dari metode Deep Learning LSTM  yang digunakan untuk melakukan prakiraan beban konsumsi listrik jangka pendek memiliki nilai RMSE (Root Mean Square Error) yang kecil Artinya tingkat akurasi dari metode Deep Learning LSTM tersebut lebih baik daripada ARIMA, hasil tersebut menunjukkan bahwa metode Deep Learning LSTM layak digunakan untuk memprakirakan beban konsumsi listrik jangka pendek di Kota Batu.

scholarly journals Pemilihan Arsitektur Terbaik pada Model Deep Learning Melalui Pendekatan Desain Eksperimen untuk Peramalan Deret Waktu Nonlinier

2019 ◽  
Vol 18 (2) ◽  
pp. 153-159
Author(s):  
Novri Suhermi ◽  
Suhartono Suhartono ◽  
I Made Gde Meranggi Dana ◽  
Dedy Dwi Prastyo
Keyword(s):  
Deep Learning ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Exponential Smoothing ◽  
Mean Square ◽  
Feedforward Network ◽  
Auto Regressive ◽  
Hidden Layer

Penentuan arsitektur model deep learning yang tepat merupakan hal yang sangat esensial untukmendapatkan hasil ramalan dengan tingkat kesalahan minimum. Arsitektur deep learning meliputijumlah input dan variabel apa saja yang digunakan, jumlah hidden layer, jumlah neuron pada setiaphidden layer, dan fungsi aktivasi. Pada penelitian ini dilakukan studi simulasi pada salah satu modeldeep learning, yaitu deep feedforward network, dengan berbagai kombinasi arsitektur untukmendapatkan arsitektur paling optimum. Data yang digunakan merupakan data bangkitan yangmengikuti model nonlinier Exponential Smoothing Transition Auto-regressive (ESTAR) sebanyak 1000data, di mana 900 data digunakan sebagai data training dan 100 data digunakan sebagai datatesting. Ukuran evaluasi model yang digunakan adalah root mean square error of prediction (RMSEP).Hasil empiris yang didapatkan di antaranya, pemilihan input yang tepat dapat meningkatkanakurasi peramalan, serta pemilihan fungsi aktivasi dan kedalaman arsitektur sangat diperlukanuntuk mendapatkan hasil ramalan yang semakin optimum.

scholarly journals Approach to COVID-19 time series data using deep learning and spectral analysis methods

2021 ◽  
Vol 9 (1) ◽  
pp. 1-21
Author(s):  
Kayode Oshinubi ◽  
◽  
Augustina Amakor ◽  
Olumuyiwa James Peter ◽  
Mustapha Rachdi ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Spectral Analysis ◽  
Deep Learning ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Time Series Data ◽  
Series Data ◽  
Mean Square

<abstract> <p>This article focuses on the application of deep learning and spectral analysis to epidemiology time series data, which has recently piqued the interest of some researchers. The COVID-19 virus is still mutating, particularly the delta and omicron variants, which are known for their high level of contagiousness, but policymakers and governments are resolute in combating the pandemic's spread through a recent massive vaccination campaign of their population. We used extreme machine learning (ELM), multilayer perceptron (MLP), long short-term neural network (LSTM), gated recurrent unit (GRU), convolution neural network (CNN) and deep neural network (DNN) methods on time series data from the start of the pandemic in France, Russia, Turkey, India, United states of America (USA), Brazil and United Kingdom (UK) until September 3, 2021 to predict the daily new cases and daily deaths at different waves of the pandemic in countries considered while using root mean square error (RMSE) and relative root mean square error (rRMSE) to measure the performance of these methods. We used the spectral analysis method to convert time (days) to frequency in order to analyze the peaks of frequency and periodicity of the time series data. We also forecasted the future pandemic evolution by using ELM, MLP, and spectral analysis. Moreover, MLP achieved best performance for both daily new cases and deaths based on the evaluation metrics used. Furthermore, we discovered that errors for daily deaths are much lower than those for daily new cases. While the performance of models varies, prediction and forecasting during the period of vaccination and recent cases confirm the pandemic's prevalence level in the countries under consideration. Finally, some of the peaks observed in the time series data correspond with the proven pattern of weekly peaks that is unique to the COVID-19 time series data.</p> </abstract>

scholarly journals Hourly Forecasting of Solar Photovoltaic Power in Pakistan Using Recurrent Neural Networks

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Sohrab Khan ◽  
Faheemullah Shaikh ◽  
Mokhi Maan Siddiqui ◽  
Tanweer Hussain ◽  
Laveet Kumar ◽  
...  
Keyword(s):  
Neural Networks ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Recurrent Neural Networks ◽  
Weather Conditions ◽  
Solar Photovoltaic ◽  
Mean Square ◽  
Solar Pv ◽  
The Impact

The solar photovoltaic (PV) power forecast is crucial for steady grid operation, scheduling, and grid electricity management. In this work, numerous time series forecast methodologies, including the statistical and artificial intelligence-based methods, are studied and compared fastidiously to forecast PV electricity. Moreover, the impact of different environmental conditions for all of the algorithms is investigated. Hourly solar PV power forecasting is done to confirm the effectiveness of various models. Data used in this paper is of one entire year and is acquired from a 100 MW solar power plant, namely, Quaid-e-Azam Solar Park, Bahawalpur, Pakistan. This paper suggests recurrent neural networks (RNNs) as the best-performing forecasting model for PV power output. Furthermore, the bidirectional long-short-term memory RNN framework delivered high accuracy results in all weather conditions, especially under cloudy weather conditions where root mean square error (RMSE) was found lowest 0.0025, R square stands at 0.99, and coefficient of variation of root mean square error (RMSE) Cv was observed 0.0095%.

scholarly journals Estimation of Apple Flowering Frost Loss for Fruit Yield Based on Gridded Meteorological and Remote Sensing Data in Luochuan, Shaanxi Province, China

2021 ◽  
Vol 13 (9) ◽  
pp. 1630
Author(s):  
Yaohui Zhu ◽  
Guijun Yang ◽  
Hao Yang ◽  
Fa Zhao ◽  
Shaoyu Han ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Minimum Temperature ◽  
Price Regulation ◽  
Flowering Period ◽  
Mean Square ◽  
Prevention Measures ◽  
Agricultural Insurance

With the increase in the frequency of extreme weather events in recent years, apple growing areas in the Loess Plateau frequently encounter frost during flowering. Accurately assessing the frost loss in orchards during the flowering period is of great significance for optimizing disaster prevention measures, market apple price regulation, agricultural insurance, and government subsidy programs. The previous research on orchard frost disasters is mainly focused on early risk warning. Therefore, to effectively quantify orchard frost loss, this paper proposes a frost loss assessment model constructed using meteorological and remote sensing information and applies this model to the regional-scale assessment of orchard fruit loss after frost. As an example, this article examines a frost event that occurred during the apple flowering period in Luochuan County, Northwestern China, on 17 April 2020. A multivariable linear regression (MLR) model was constructed based on the orchard planting years, the number of flowering days, and the chill accumulation before frost, as well as the minimum temperature and daily temperature difference on the day of frost. Then, the model simulation accuracy was verified using the leave-one-out cross-validation (LOOCV) method, and the coefficient of determination (R2), the root mean square error (RMSE), and the normalized root mean square error (NRMSE) were 0.69, 18.76%, and 18.76%, respectively. Additionally, the extended Fourier amplitude sensitivity test (EFAST) method was used for the sensitivity analysis of the model parameters. The results show that the simulated apple orchard fruit number reduction ratio is highly sensitive to the minimum temperature on the day of frost, and the chill accumulation and planting years before the frost, with sensitivity values of ≥0.74, ≥0.25, and ≥0.15, respectively. This research can not only assist governments in optimizing traditional orchard frost prevention measures and market price regulation but can also provide a reference for agricultural insurance companies to formulate plans for compensation after frost.

scholarly journals Low Cost Automatic Reconstruction of Tree Structure by AdQSM with Terrestrial Close-Range Photogrammetry

Forests ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1020
Author(s):  
Yanqi Dong ◽  
Guangpeng Fan ◽  
Zhiwu Zhou ◽  
Jincheng Liu ◽  
Yongguo Wang ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Low Cost ◽  
Reference Value ◽  
Tree Height ◽  
Point Clouds ◽  
Tree Structure ◽  
Mean Square ◽  
Tree Model

The quantitative structure model (QSM) contains the branch geometry and attributes of the tree. AdQSM is a new, accurate, and detailed tree QSM. In this paper, an automatic modeling method based on AdQSM is developed, and a low-cost technical scheme of tree structure modeling is provided, so that AdQSM can be freely used by more people. First, we used two digital cameras to collect two-dimensional (2D) photos of trees and generated three-dimensional (3D) point clouds of plot and segmented individual tree from the plot point clouds. Then a new QSM-AdQSM was used to construct tree model from point clouds of 44 trees. Finally, to verify the effectiveness of our method, the diameter at breast height (DBH), tree height, and trunk volume were derived from the reconstructed tree model. These parameters extracted from AdQSM were compared with the reference values from forest inventory. For the DBH, the relative bias (rBias), root mean square error (RMSE), and coefficient of variation of root mean square error (rRMSE) were 4.26%, 1.93 cm, and 6.60%. For the tree height, the rBias, RMSE, and rRMSE were—10.86%, 1.67 m, and 12.34%. The determination coefficient (R2) of DBH and tree height estimated by AdQSM and the reference value were 0.94 and 0.86. We used the trunk volume calculated by the allometric equation as a reference value to test the accuracy of AdQSM. The trunk volume was estimated based on AdQSM, and its bias was 0.07066 m3, rBias was 18.73%, RMSE was 0.12369 m3, rRMSE was 32.78%. To better evaluate the accuracy of QSM’s reconstruction of the trunk volume, we compared AdQSM and TreeQSM in the same dataset. The bias of the trunk volume estimated based on TreeQSM was −0.05071 m3, and the rBias was −13.44%, RMSE was 0.13267 m3, rRMSE was 35.16%. At 95% confidence interval level, the concordance correlation coefficient (CCC = 0.77) of the agreement between the estimated tree trunk volume of AdQSM and the reference value was greater than that of TreeQSM (CCC = 0.60). The significance of this research is as follows: (1) The automatic modeling method based on AdQSM is developed, which expands the application scope of AdQSM; (2) provide low-cost photogrammetric point cloud as the input data of AdQSM; (3) explore the potential of AdQSM to reconstruct forest terrestrial photogrammetric point clouds.

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