Comparison of evapotranspiration models

Actual evapotranspiration of wheat crop during different year from 1978-79 to 1992-93 was measured daily in Varanasi, Uttar Pradesh using lysimeter. In this study three evapotranspiration computing models namely Doorenbos and Pruitt, Thornthwaite and Soil Plant Atmosphere Water (SPAW) have been used. Comparisons of these three methods show that the SPAW model is better than the other two methods for evapotraspiration estimation. In the present study the MBE (Mean-Bias-Error), RMSE (Root Mean Square Error) and t-statistic have also been obtained for better evaluations of a model performance.

Research on Teaching Practice of Blended Higher Education Based on Deep Learning Route

This paper establishes a hybrid education teaching practice quality evaluation system in colleges and constructs a hybrid teaching quality evaluation model based on a deep belief network. Karl Pearson correlation coefficient and root mean square error (RMSE) indicators are used to measure the closeness and fluctuation between the effective online teaching quality evaluation results evaluated by this method and the actual teaching quality results. The experimental results show the following: (1) As the number of iterations increases, the fitting error of the DBN model decreases significantly. When the number of iterations reaches 20, the fitting error of the DBN model stabilizes and decreases to below 0.01. The experimental results show that the model used in this method has good learning and training performance, and the fitting error is low. (2) The evaluation correlation coefficients are all greater than 0.85, and the root mean square error of the evaluation is less than 0.45, indicating that the evaluation results of this method are similar to the actual evaluation level and have small errors, which can be effectively applied to online teaching quality evaluation in colleges and universities.

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

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%.

Pengamanan Lontar Digital Dengan Tanda Tangan Digital Menggunakan Algoritma RSA

Lontar is a relic of cultural heritage whose basic source of manufacture is from rontal or tal leaves containing evidence of all records of aspects of ancient historical life which include historical values, religion, philosophy, medicine, literature and other sciences so that their sustainability needs to be maintained. Security of digital lontar will make it easier to preserve a lontar work so that it is not changed or falsified by irresponsible parties, where digital lontar in PDF format will be given a digital signature to maintain the authenticity of the document. Documents that are signed will be difficult for other parties to change, if the contents of a digital ejection are changed it will cause the digital signature to change. Based on the research conducted, from the results of testing the security of digital ejection with digital signatures using the RSA algorithm, the test results from RMSE (Root Mean Square Error) for description results with an average of 69.7794143. The larger or random the description results, the more complex the description results will be.

DEVELOPMENT OF RAINFALL FORECASTING MODEL USING MACHINE LEARNING WITH SINGULAR SPECTRUM ANALYSIS

Agriculture is the key point for survival for developing nations like India. For farming, rainfall is generally significant. Rainfall updates are help for evaluate water assets, farming, ecosystems and hydrology. Nowadays rainfall anticipation has become a foremost issue. Forecast of rainfall offers attention to individuals and knows in advance about rainfall to avoid potential risk to shield their crop yields from severe rainfall. This study intends to investigate the dependability of integrating a data pre-processing technique called singular-spectrum-analysis (SSA) with supervised learning models called least-squares support vector regression (LS-SVR), and Random-Forest (RF), for rainfall prediction. Integrating SSA with LS-SVR and RF, the combined framework is designed and contrasted with the customary approaches (LS-SVR and RF). The presented frameworks were trained and tested utilizing a monthly climate dataset which is separated into 80:20 ratios for training and testing respectively. Performance of the model was assessed using Root Mean Square Error (RMSE) and Nash–Sutcliffe Efficiency (NSE) and the proposed model produces the values as 71.6 %, 90.2 % respectively. Experimental outcomes illustrate that the proposed model can productively predict the rainfall. ABSTRAK:Pertanian adalah titik utama kelangsungan hidup negara-negara membangun seperti India. Untuk pertanian, curah hujan pada amnya ketara. Kemas kini hujan adalah bantuan untuk menilai aset air, pertanian, ekosistem dan hidrologi. Kini, jangkaan hujan telah menjadi isu utama. Ramalan hujan memberikan perhatian kepada individu dan mengetahui terlebih dahulu mengenai hujan untuk menghindari potensi risiko untuk melindungi hasil tanaman mereka dari hujan lebat. Kajian ini bertujuan untuk menyelidiki kebolehpercayaan mengintegrasikan teknik pra-pemprosesan data yang disebut analisis-spektrum tunggal (SSA) dengan model pembelajaran yang diawasi yang disebut regresi vektor sokongan paling rendah (LS-SVR), dan Random-Forest (RF), ramalan hujan. Menggabungkan SSA dengan LS-SVR dan RF, kerangka gabungan dirancang dan dibeza-bezakan dengan pendekatan biasa (LS-SVR dan RF). Kerangka kerja yang disajikan dilatih dan diuji dengan menggunakan set data iklim bulanan yang masing-masing dipisahkan menjadi nisbah 80:20 untuk latihan dan ujian. Prestasi model dinilai menggunakan Root Mean Square Error (RMSE) dan Nash – Sutcliffe Efficiency (NSE) dan model yang dicadangkan menghasilkan nilai masing-masing sebanyak 71.6%, 90.2%. Hasil eksperimen menggambarkan bahawa model yang dicadangkan dapat meramalkan hujan secara produktif.

A GENERIC FUZZY-BASED RECOMMENDATION APPROACH (GFBRA)

Recommender Systems aim to automatically provide users with personalized information in an overloaded search space. To dual with vagueness and imprecision problems in RS, several researches have been proposed fuzzy based approaches. Even though, these works have incorporated experimental evaluation; they were used in different recommendation scenarios which makes it difficult to have a fair comparison between them. Also, some of them performed an items and/or users clustering before generating recommendations. For this reason they need additional information such as item attributes or trust between users which are not always available. In this paper, we propose to use fuzzy set techniques to predict the rating of a target user for each unrated item. It uses the target user's history in addition with rating of similar users which allows to the target user to contribute in the recommendation process. Experimental results on several datasets seem to be promising in term of MAE (Mean Average Error), RMSE (Root Mean Square Error), accuracy, precision, recall and F-measure.

Fatigue Crack Evaluation with the Guided Wave–Convolutional Neural Network Ensemble and Differential Wavelet Spectrogram

On-line fatigue crack evaluation is crucial for ensuring the structural safety and reducing the maintenance costs of safety-critical systems. Among structural health monitoring (SHM), guided wave (GW)-based SHM has been deemed as one of the most promising techniques. However, the traditional damage index-based method and machine learning methods require manual processing and selection of GW features, which depend highly on expert knowledge and are easily affected by complicated uncertainties. Therefore, this paper proposes a fatigue crack evaluation framework with the GW–convolutional neural network (CNN) ensemble and differential wavelet spectrogram. The differential time–frequency spectrogram between the baseline signal and the monitoring signal is processed as the CNN input with the complex Gaussian wavelet transform. Then, an ensemble of CNNs is trained to jointly determine the crack length. Real fatigue tests on complex lap joint structures were carried out to validate the proposed method, in which several structures were tested preliminarily for collecting the training dataset and a new structure was adopted for testing. The root mean square error of the training dataset is 1.4 mm. Besides, the root mean square error of the evaluated crack length in the testing lap joint structure was 1.7 mm, showing the effectiveness of the proposed method.

Comparisons between intake values observed and predicted by nutritional systems for confined nellore cattle

This study compared the dry matter intake (DMI) of Nellore heifers and bulls in the feedlot, predicted by the BR-Corte (2010 and 2016) and NRC (2000) nutritional systems. Hence, two experiments were conducted in a completely randomized design. The first one used 47 Nellore bulls, not castrated, with an average initial weight of 413 kg, and 19 months of age. The second experiment used 24 Nellore heifers with an average initial weight of 300 kg and 23 months of age. The accuracy and approximation of the DMI estimates by the nutritional systems were adjusted with the simple linear regression model and the root mean square error of prediction (RMSEP). The DMI was 8.06 kg day-1 for Nellore heifers and 11.54 kg day-1 for bulls, which are higher than the values ​​ predicted by the nutritional systems. The NRC (2000) and BR-Corte (2010 and 2016) underestimated DMI in 20.84, 20.09, and 19.35% for heifers and 28.07, 16.20, and 11.78% for bulls, respectively. It was concluded that the BR-Corte 2010 and 2016 were the most suitable models to estimate the DMI of Nellore heifers and bulls for higher precision and accuracy.

Forecasting peak surface gust wind in association with thunderstorm activity during pre-monsoon season at Delhi

In this study, an attempt is made to develop an objective method for forecasting the direction and speed of the gusty winds associated with thunderstorms at Delhi. For this purpose, surface and upper-air data for April, May and June (AMJ) for the years 1985-90 are utilized. Multiple regression equations are developed for forecasting the direction and speed of the gusty winds, using stepwise screening method, for which a total of 181 potential predictors are utilized. The developed dynamical-statistical models are tested with independent data sets of 1994 and 1995 for April, May and June. The dynamical-statistical models give satisfactory results with the developmental as well as the independent data sets. The root mean square error of the direction vary between 58° and 77° and the speed forecast vary between 9 and 12 knots. Possible reasons for large deviations of the forecast, noticed on a very few occasions, have also been examined.

Structural validity of the physical fitness test battery

Purpose: of the study was to examine the validity of the Army Combat Fitness Test tests on a sample of air defense personnel in the Ukrainian Ground Forces. Material and methods. The respondents to this study were 271 air defense servicemen of the ground forces aged 18 to 40 years (73 cadets of the Ivan Kozhedub Kharkiv National Air Force University and 198 military personnel). The structural validity was evaluated using a confirmatory factor analysis. Results. Compliance was achieved with the two-factor model obtained in the course of exploratory factor analysis, as evidenced by the following indixes: χ2 (8, Critical N = 465.29) = 10.43; χ2 / df = 1.303; Non-Normed Fit Index = 0.98; Normed Fit Index = 0.97; Root Mean Square Error of Approximation = 0.035 (90 Percent Confidence Interval for Root Mean Square Error of Approximation = (0.0; 0.088), Comparative Fit Index = 0.99. In addition, all factor loadings were statistically significant at the p < 0.01 level, that indicates that these two factors were well designed at every stage. Correlation between factors was weak, which confirms the discriminant validity of the test. The significant correlation found between the items and the overall test score confirmed the validity of the test. Conclusions. It was found that Army Combat Fitness Test is a suitable tool for evaluating the physical fitness condition of air defense personnel into the Ground Forces. The dilemmas about the possible use of Army Combat Fitness Test for all age groups of military personnel regardless of gender require further study.