Nonlinear Rainfall Yearly Prediction based on Autoregressive Artificial Neural Networks Model in Central Jordan using Data Records: 1938-2018

Sharadqah, Suhail; Mohammad, Azadi Mobaraki; Marbello, Ramiro; Mercedes, Soraya

doi:10.14569/ijacsa.2021.0120231

Cited by 9 publications

(5 citation statements)

References 24 publications

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“…Future work can be conducted in many directions and applications. The use of OPC communication channel would have interesting future directions in many applications such as power [22][23][24][25], health [26][27][28][29][30][31], communication [32][33][34][35][36][37], AI applications [38][39][40][41][42][43], and optimization [44,45].…”

Section: • Discussionmentioning

confidence: 99%

Online Parameter Estimation of DC-DC Converter through OPC Communication Channel

Obeidat¹,

Anani²,

Mansour³

2021

IJACSA

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System identification is a very powerful tool for determining the system model and parameters from sets of observable input and output data. Once the system parameters are obtained, the system dynamic behavior, including all the system characteristics (time constant, overshoot, settling time, etc.) can be accessed and evaluated. Despite the difficulty and communication channel lag, online parameter estimation outperforms offline system identification due to the ability to remotely monitor and control the system as well as improve the system's controller, making it more accurate and reliable. With the extreme development in technology, the importance of combining wireless networks with closed automatic control systems has emerged. This connection facilitates communication processes between the different units in the control for remotely controlled of the output. However, there are some errors affecting such system resulted from communication channel, A/D and D/A conversion process, identification process, or the existence of adaptive weight Gaussian noise. In this paper, the errors were investigated using real system, and then a suitable controller was tuned and optimized in order to reduce and eliminate various errors. The results show excellent dynamic behavior of the system under transmitting and receiving process.

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Section: • Discussionmentioning

confidence: 99%

Online Parameter Estimation of DC-DC Converter through OPC Communication Channel

Obeidat¹,

Anani²,

Mansour³

2021

IJACSA

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“…[8] proposed an artificial neural network model to predict rainfall for Columbia summer season. [7] have used Jordan rainfall dataset to predict the yearly rainfall using artificial neural network. [9] have used BP neural network to forecast the rainfall for coastal areas.…”

Section: Related Workmentioning

confidence: 99%

Forecast Rainfall Density by Utilizing Machine Learning Models

Hsu¹,

Sharma²

2023

World Congress on Civil, Structural, and Environmental Engineering

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Organizations can use weather forecasting to help with decision-making when it comes to preventing disasters. Forecasting rain is challenging since weather conditions are always unpredictable in general. The prediction of rainfall uses a variety of methodologies, including statistical, hybrid, and physical approaches. In this research, we have implemented various machine learning models such as Logistic Regression (LR), Random Forest (RF), and Multi-Layer Perceptron (MLP) to predict the density of rainfall. This study has used Taiwan Ruiyan rainfall hourly dataset from 1998 to 2018 which contains five features like Air Pressure, Humidity, Temperature, Windspeed, and Wind Direction to predict the rainfall density such as low, medium, and heavy rainfall. The results data in this study are compared using statistical metrics like AUC, accuracy, recall, precision, and F1-score. The Random Forest, and Multi-Layer Perceptron models, had the highest accuracy scores of 0.71, accurately predicting the results. This study offers a comprehensive overview of several methods and their rainfall density predictions. By comparing these models, we can decide which one is best for predicting rainfall. The suggested work is extensively used in a variety of agriculture and civil applications, including hazard prediction, prevention, operational planning, and many more.

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“…Since a neural network is a method with strong nonlinear adaptive information processing ability, it can extract the feature information from the input training samples and store the conversion relationship between the input and the output inside the network to achieve the goals of recognition and prediction [12]. For example, Suhail Sharadqah et al (2021) tested rainfall data using NAR models [13]. Gunathilake Miyuru B et al (2021) used artificial neural network combined with remote sensing information for precipitation estimation [14].…”

Section: Introductionmentioning

confidence: 99%

Research on a Rainfall Prediction Model in Guizhou Based on Raindrop Spectra

Wang,

An,

Wang

et al. 2024

Atmosphere

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Our study and analysis of the distribution differences in raindrop spectra in a Guizhou precipitation prediction model were of great significance for understanding precipitation microphysical processes and improving radar quantitative precipitation prediction. This article selected the Dafang, Majiang, and Luodian stations at different altitudes in Guizhou and analyzed the distribution characteristics of precipitation particles at different altitudes. This article used precipitation data from the new-generation Doppler weather radar, OTT-Parsivel laser raindrop spectrometer, and automatic meteorological observation stations in Guiyang via M-P and GAMMA and established methods to fit the particle size of raindrop spectrum precipitation. Based on the LSTM neural network method, we constructed a precipitation prediction model for Guizhou and conducted performance testing. The results show that (1) the precipitation particles at the three stations are all concentrated in small particle size areas, with a peak value of 0.312 mm and a final falling velocity of 1–5 m/s, and the particle size increases with a decreasing altitude. The contribution rate to the density of particles with a precipitation particle size of less than 1 mm exceeds 80% and decreases with a decreasing altitude. The average volume diameter of precipitation particles has the highest correlation with the precipitation intensity. (2) In the fitting of the raindrop spectrum distribution, the GAMMA distribution fitted by the three stations has a better effect and the fitting effect gradually improves with an increasing altitude. (3) In precipitation prediction for convective clouds and stratiform clouds, the 60 min prediction results are the most consistent with the actual precipitation, with correlation coefficients of 0.9287 and 0.9257, respectively, indicating that the prediction has high reliability.

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Nonlinear Rainfall Yearly Prediction based on Autoregressive Artificial Neural Networks Model in Central Jordan using Data Records: 1938-2018

Cited by 9 publications

References 24 publications

Online Parameter Estimation of DC-DC Converter through OPC Communication Channel

Online Parameter Estimation of DC-DC Converter through OPC Communication Channel

Forecast Rainfall Density by Utilizing Machine Learning Models

Research on a Rainfall Prediction Model in Guizhou Based on Raindrop Spectra

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