scholarly journals Real-Time Rainfall Forecasts Based on Radar Reflectivity during Typhoons: Case Study in Southeastern Taiwan

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1421
Author(s):  
Chih-Chiang Wei ◽  
Chen-Chia Hsu
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Elevation Angle ◽  
Radar Reflectivity ◽  
Weather Station ◽  
Rainfall Forecasting ◽  
Data Set ◽  
Current Time ◽  
Rainfall Prediction ◽  
Extreme Gradient Boosting

This study developed a real-time rainfall forecasting system that can predict rainfall in a particular area a few hours before a typhoon’s arrival. The reflectivity of nine elevation angles obtained from the volume coverage pattern 21 Doppler radar scanning strategy and ground-weather data of a specific area were used for accurate rainfall prediction. During rainfall prediction and analysis, rainfall retrievals were first performed to select the optimal radar scanning elevation angle for rainfall prediction at the current time. Subsequently, forecasting models were established using a single reflectivity and all elevation angles (10 prediction submodels in total) to jointly predict real-time rainfall and determine the optimal predicted values. This study was conducted in southeastern Taiwan and included three onshore weather stations (Chenggong, Taitung, and Dawu) and one offshore weather station (Lanyu). Radar reflectivities were collected from Hualien weather surveillance radar. The data for a total of 14 typhoons that affected the study area in 2008–2017 were collected. The gated recurrent unit (GRU) neural network was used to establish the forecasting model, and extreme gradient boosting and multiple linear regression were used as the benchmarks. Typhoons Nepartak, Meranti, and Megi were selected for simulation. The results revealed that the input data set merged with weather-station data, and radar reflectivity at the optimal elevation angle yielded optimal results for short-term rainfall forecasting. Moreover, the GRU neural network can obtain accurate predictions 1, 3, and 6 h before typhoon occurrence.

scholarly journals Research on Real-Time Multiple Single Garbage Classification Based on Convolutional Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Jian-ye Yuan ◽  
Xin-yuan Nan ◽  
Cheng-rong Li ◽  
Le-le Sun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Environmental Science ◽  
Fine Tuning ◽  
Classification Model ◽  
Classification Models ◽  
Data Set ◽  
Classification Tasks ◽  
Accuracy Rates

Considering that the garbage classification is urgent, a 23-layer convolutional neural network (CNN) model is designed in this paper, with the emphasis on the real-time garbage classification, to solve the low accuracy of garbage classification and recycling and difficulty in manual recycling. Firstly, the depthwise separable convolution was used to reduce the Params of the model. Then, the attention mechanism was used to improve the accuracy of the garbage classification model. Finally, the model fine-tuning method was used to further improve the performance of the garbage classification model. Besides, we compared the model with classic image classification models including AlexNet, VGG16, and ResNet18 and lightweight classification models including MobileNetV2 and SuffleNetV2 and found that the model GAF_dense has a higher accuracy rate, fewer Params, and FLOPs. To further check the performance of the model, we tested the CIFAR-10 data set and found the accuracy rates of the model (GAF_dense) are 0.018 and 0.03 higher than ResNet18 and SufflenetV2, respectively. In the ImageNet data set, the accuracy rates of the model (GAF_dense) are 0.225 and 0.146 higher than Resnet18 and SufflenetV2, respectively. Therefore, the garbage classification model proposed in this paper is suitable for garbage classification and other classification tasks to protect the ecological environment, which can be applied to classification tasks such as environmental science, children’s education, and environmental protection.

scholarly journals Rainfall Forecasting Using Various Artificial Neural Network Techniques - A Review

2021 ◽  
pp. 506-526
Author(s):  
Nisha Thakur ◽  
Sanjeev Karmakar ◽  
Sunita Soni
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Moving Average ◽  
Weather Prediction ◽  
Optimization Technique ◽  
Back Propagation ◽  
Rainfall Forecasting ◽  
Rainfall Prediction ◽  
Artificial Neural ◽  
Review Reports

The present review reports the work done by the various authors towards rainfall forecasting using the different techniques within Artificial Neural Network concepts. Back-Propagation, Auto-Regressive Moving Average (ARIMA), ANN , K- Nearest Neighbourhood (K-NN), Hybrid model (Wavelet-ANN), Hybrid Wavelet-NARX model, Rainfall-runoff models, (Two-stage optimization technique), Adaptive Basis Function Neural Network (ABFNN), Multilayer perceptron, etc., algorithms/technologies were reviewed. A tabular representation was used to compare the above-mentioned technologies for rainfall predictions. In most of the articles, training and testing, accuracy was found more than 95%. The rainfall prediction done using the ANN techniques was found much superior to the other techniques like Numerical Weather Prediction (NWP) and Statistical Method because of the non-linear and complex physical conditions affecting the occurrence of rainfall.

scholarly journals Sensitivity of GNSS tropospheric gradients to processing options

2019 ◽  
Vol 37 (3) ◽  
pp. 429-446 ◽  
Author(s):  
Michal Kačmařík ◽  
Jan Douša ◽  
Florian Zus ◽  
Pavel Václavovic ◽  
Kyriakos Balidakis ◽  
...  
Keyword(s):  
Real Time ◽  
Elevation Angle ◽  
Mapping Function ◽  
Global Navigation Satellite Systems ◽  
Gradient Estimates ◽  
Post Processing ◽  
Data Set ◽  
Mapping Functions ◽  
Gradient Mapping

Abstract. An analysis of processing settings impacts on estimated tropospheric gradients is presented. The study is based on the benchmark data set collected within the COST GNSS4SWEC action with observations from 430 Global Navigation Satellite Systems (GNSS) reference stations in central Europe for May and June 2013. Tropospheric gradients were estimated in eight different variants of GNSS data processing using precise point positioning (PPP) with the G-Nut/Tefnut software. The impacts of the gradient mapping function, elevation cut-off angle, GNSS constellation, observation elevation-dependent weighting and real-time versus post-processing mode were assessed by comparing the variants by each to other and by evaluating them with respect to tropospheric gradients derived from two numerical weather models (NWMs). Tropospheric gradients estimated in post-processing GNSS solutions using final products were in good agreement with NWM outputs. The quality of high-resolution gradients estimated in (near-)real-time PPP analysis still remains a challenging task due to the quality of the real-time orbit and clock corrections. Comparisons of GNSS and NWM gradients suggest the 3∘ elevation angle cut-off and GPS+GLONASS constellation for obtaining optimal gradient estimates provided precise models for antenna-phase centre offsets and variations, and tropospheric mapping functions are applied for low-elevation observations. Finally, systematic errors can affect the gradient components solely due to the use of different gradient mapping functions, and still depending on observation elevation-dependent weighting. A latitudinal tilting of the troposphere in a global scale causes a systematic difference of up to 0.3 mm in the north-gradient component, while large local gradients, usually pointing in a direction of increasing humidity, can cause differences of up to 1.0 mm (or even more in extreme cases) in any component depending on the actual direction of the gradient. Although the Bar-Sever gradient mapping function provided slightly better results in some aspects, it is not possible to give any strong recommendation on the gradient mapping function selection.

scholarly journals An artificial neural network model for rainfall forecasting in Bangkok, Thailand

2009 ◽  
Vol 13 (8) ◽  
pp. 1413-1425 ◽  
Author(s):  
N. Q. Hung ◽  
M. S. Babel ◽  
S. Weesakul ◽  
N. K. Tripathi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Real Time ◽  
Input Parameter ◽  
Rain Gauge ◽  
Ann Model ◽  
Rainfall Forecasting ◽  
Forecast Performance ◽  
Hourly Data ◽  
Artificial Neural

Abstract. This paper presents a new approach using an Artificial Neural Network technique to improve rainfall forecast performance. A real world case study was set up in Bangkok; 4 years of hourly data from 75 rain gauge stations in the area were used to develop the ANN model. The developed ANN model is being applied for real time rainfall forecasting and flood management in Bangkok, Thailand. Aimed at providing forecasts in a near real time schedule, different network types were tested with different kinds of input information. Preliminary tests showed that a generalized feedforward ANN model using hyperbolic tangent transfer function achieved the best generalization of rainfall. Especially, the use of a combination of meteorological parameters (relative humidity, air pressure, wet bulb temperature and cloudiness), the rainfall at the point of forecasting and rainfall at the surrounding stations, as an input data, advanced ANN model to apply with continuous data containing rainy and non-rainy period, allowed model to issue forecast at any moment. Additionally, forecasts by ANN model were compared to the convenient approach namely simple persistent method. Results show that ANN forecasts have superiority over the ones obtained by the persistent model. Rainfall forecasts for Bangkok from 1 to 3 h ahead were highly satisfactory. Sensitivity analysis indicated that the most important input parameter besides rainfall itself is the wet bulb temperature in forecasting rainfall.

scholarly journals Neural network based approximation of muscle and joint contact forces during jumping and landing

2019 ◽  
Author(s):  
Daniel Cleather
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Real Time ◽  
Contact Forces ◽  
Data Set ◽  
Video Record ◽  
Musculoskeletal Models ◽  
The Neural Network ◽  
Joint Contact ◽  
The Neural Networks

Musculoskeletal models have been used to estimate the muscle and joint contact forces expressed during movement. One limitation of this approach, however, is that such models are computationally demanding, which limits the possibility of using them for real-time feedback. One solution to this problem is to train a neural network to approximate the performance of the model, and then to use the neural network to give real-time feedback. In this study, neural networks were trained to approximate the FreeBody musculoskeletal model for jumping and landing tasks. The neural networks were better able to approximate jumping than landing, which was probably a result of the greater variability in the landing data set used in this study. In addition, a neural network that was based on a reduced set of inputs was also trained to approximate the outputs of FreeBody during a landing task. These results demonstrate the feasibility of using neural networks to approximate the results of musculoskeletal models in order to provide real-time feedback. In addition, these neural networks could be based upon a reduced set of kinematic variables taken from a 2-dimensional video record, making the implementation of mobile applications a possibility.

scholarly journals Traffic Sign Classification Using Convolutional Neural Network

2021 ◽  
pp. 01-10
Author(s):  
Pranav Kale ◽  
Mayuresh Panchpor ◽  
Saloni Dingore ◽  
Saloni Gaikwad ◽  
Prof. Dr. Laxmi Bewoor
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Autonomous Driving ◽  
Time Data ◽  
Traffic Sign ◽  
Real World Data ◽  
Data Set ◽  
Proposed Model ◽  
Validation Set

In today's world, deep learning fields are getting boosted with increasing speed. Lot of innovations and different algorithms are being developed. In field of computer vision, related to autonomous driving sector, traffic signs play an important role to provide real time data of an environment. Different algorithms were developed to classify these Signs. But performance still needs to improve for real time environment. Even the computational power required to train such model is high. In this paper, Convolutional Neural Network model is used to Classify Traffic Sign. The experiments are conducted on a real-world data set with images and videos captured from ordinary car driving as well as on GTSRB dataset [15] available on Kaggle. This proposed model is able to outperform previous models and resulted with accuracy of 99.6% on validation set. This idea has been granted Innovation Patent by Australian IP to Authors of this Research Paper. [24]

scholarly journals Automated predictive analytics tool for rainfall forecasting

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Maulin Raval ◽  
Pavithra Sivashanmugam ◽  
Vu Pham ◽  
Hardik Gohel ◽  
Ajeet Kaushik ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Predictive Analytics ◽  
Weather Conditions ◽  
Machine Learning Algorithms ◽  
Rainfall Data ◽  
Machine Learning Techniques ◽  
Rainfall Forecasting ◽  
Rainfall Prediction ◽  
Prediction Techniques

AbstractAustralia faces a dryness disaster whose impact may be mitigated by rainfall prediction. Being an incredibly challenging task, yet accurate prediction of rainfall plays an enormous role in policy making, decision making and organizing sustainable water resource systems. The ability to accurately predict rainfall patterns empowers civilizations. Though short-term rainfall predictions are provided by meteorological systems, long-term prediction of rainfall is challenging and has a lot of factors that lead to uncertainty. Historically, various researchers have experimented with several machine learning techniques in rainfall prediction with given weather conditions. However, in places like Australia where the climate is variable, finding the best method to model the complex rainfall process is a major challenge. The aim of this paper is to: (a) predict rainfall using machine learning algorithms and comparing the performance of different models. (b) Develop an optimized neural network and develop a prediction model using the neural network (c) to do a comparative study of new and existing prediction techniques using Australian rainfall data. In this paper, rainfall data collected over a span of ten years from 2007 to 2017, with the input from 26 geographically diverse locations have been used to develop the predictive models. The data was divided into training and testing sets for validation purposes. The results show that both traditional and neural network-based machine learning models can predict rainfall with more precision.

Comparative study on performance of different artificial neural network methods for prediction of the Covid19

foresight ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Alireza Sedighi Fard
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Short Term Memory ◽  
Polynomial Regression ◽  
World Health ◽  
Comparative Approach ◽  
Data Set ◽  
Current Time ◽  
Content Type ◽  
Artificial Neural

Purpose This study aims to compare many artificial neural network (ANN) methods to find out which method is better for the prediction of Covid19 number of cases in N steps ahead of the current time. Therefore, the authors can be more ready for similar issues in the future. Design/methodology/approach The authors are going to use many ANNs in this study including, five different long short-term memory (LSTM) methods, polynomial regression (from degree 2 to 5) and online dynamic unsupervised feedforward neural network (ODUFFNN). The authors are going to use these networks over a data set of Covid19 number of cases gathered by World Health Organization. After 1,000 epochs for each network, the authors are going to calculate the accuracy of each network, to be able to compare these networks by their performance and choose the best method for the prediction of Covid19. Findings The authors concluded that for most of the cases LSTM could predict Covid19 cases with an accuracy of more than 85% after LSTM networks ODUFFNN had medium accuracy of 45% but this network is highly flexible and fast computing. The authors concluded that polynomial regression cant is a good method for the specific purpose. Originality/value Considering the fact that Covid19 is a new global issue, less studies have been conducted with a comparative approach toward the prediction of Covid19 using ANN methods to introduce the best model of the prediction of this virus.

Application of Machine Learning to Interpret Steady State Drainage Relative Permeability Experiments

2021 ◽  
Author(s):  
Eric Sonny Mathew ◽  
Moussa Tembely ◽  
Waleed AlAmeri ◽  
Emad W. Al-Shalabi ◽  
Abdul Ravoof Shaik
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Experimental Data ◽  
Steady State ◽  
Relative Permeability ◽  
Learning Model ◽  
Gradient Boosting ◽  
Data Set ◽  
Machine Learning Model ◽  
Extreme Gradient Boosting

Abstract A meticulous interpretation of steady-state or unsteady-state relative permeability (Kr) experimental data is required to determine a complete set of Kr curves. In this work, three different machine learning models was developed to assist in a faster estimation of these curves from steady-state drainage coreflooding experimental runs. The three different models that were tested and compared were extreme gradient boosting (XGB), deep neural network (DNN) and recurrent neural network (RNN) algorithms. Based on existing mathematical models, a leading edge framework was developed where a large database of Kr and Pc curves were generated. This database was used to perform thousands of coreflood simulation runs representing oil-water drainage steady-state experiments. The results obtained from these simulation runs, mainly pressure drop along with other conventional core analysis data, were utilized to estimate Kr curves based on Darcy's law. These analytically estimated Kr curves along with the previously generated Pc curves were fed as features into the machine learning model. The entire data set was split into 80% for training and 20% for testing. K-fold cross validation technique was applied to increase the model accuracy by splitting the 80% of the training data into 10 folds. In this manner, for each of the 10 experiments, 9 folds were used for training and the remaining one was used for model validation. Once the model is trained and validated, it was subjected to blind testing on the remaining 20% of the data set. The machine learning model learns to capture fluid flow behavior inside the core from the training dataset. The trained/tested model was thereby employed to estimate Kr curves based on available experimental results. The performance of the developed model was assessed using the values of the coefficient of determination (R2) along with the loss calculated during training/validation of the model. The respective cross plots along with comparisons of ground-truth versus AI predicted curves indicate that the model is capable of making accurate predictions with error percentage between 0.2 and 0.6% on history matching experimental data for all the three tested ML techniques (XGB, DNN, and RNN). This implies that the AI-based model exhibits better efficiency and reliability in determining Kr curves when compared to conventional methods. The results also include a comparison between classical machine learning approaches, shallow and deep neural networks in terms of accuracy in predicting the final Kr curves. The various models discussed in this research work currently focusses on the prediction of Kr curves for drainage steady-state experiments; however, the work can be extended to capture the imbibition cycle as well.

scholarly journals Development of a Small Intelligent Weather Station for Agricultural Applications

2021 ◽  
Author(s):  
Yi-Hua Chung ◽  
Jun-Fu Huang ◽  
Yuan-Chen Hu ◽  
Chen-Kang Huang
Keyword(s):  
Agricultural Production ◽  
The Other ◽  
Weather Station ◽  
Rainfall Forecasting ◽  
Rainfall Prediction ◽  
Weather Bureau ◽  
Lightning Detection ◽  
Agricultural Applications ◽  
Central Weather Bureau ◽  
Decision Mode

It is known that climate change causes a decrease in the profit gained from agricultural production. This work designs and establishes weather boxes equipped with functions of rainfall prediction, frosting forecast, and lightning detection. With the wireless connection and the build-in decision mode, weather boxes can deliver early-warning by sending texting messages to the users and actuating the corresponding action to response the extreme climate. To implement rainfall and frosting prognostication, two different datasets are analyzed by the technology of data mining. One of the datasets is acquired from the Central Weather Bureau, and the other is from the proposed weather box monitoring the agricultural environment. From the experimental results, the prediction model constructed from the data which is collected by the proposed weather box exhibits a higher accuracy in rainfall forecasting than those based on the Central Weather Bureau.

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