scholarly journals Effective Procedure to Predict Rainfall Conditions using Hybrid Machine Learning Strategies

2021 ◽  
Vol 12 (6) ◽  
pp. 209-216
Author(s):  
S.Sakthivel Et.al
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Learning Strategies ◽  
Learning Strategy ◽  
Human Life ◽  
Machine Learning Algorithms ◽  
Rainfall Prediction ◽  
Long Period ◽  
Hybrid Machine ◽  
Limited Period

In the present information technology stream supports many natural disaster prediction schemes to save several people from disaster scenarios. In such case, rainfall prediction and analysis is the most important concern to take care as well as the prediction of high rainfall saves many individual's life and their assets. This kind of rainfall prediction schemes provides a facilitation to take respective precautions to avoid huge damages further. The rainfall predictions are categorized into two different variants such as Limited Period Rainfall Prediction and the long period Continuous Rainfall Prediction. Several past analysis and literatures provide accurate predictions for limited period rainfall but the major problem is to identify or predict the continuous long period rainfall. This kind of drawbacks leads many researchers to work on this domain and predict the rainfall status exactly for both limited period as well as long period continues rainfall. In this paper, a new hybrid machine learning strategy is implemented to predict the rainfall status exactly, in which the proposed methodology is named as Intense Neural Network Mining (INNM). This proposed approach of INNM analyze the rainfall prediction scenario based on two different machine learning logics such as Back Propagation Neural Network and the Rapid Miner. The general machine learning algorithms train the machine with respect to the dataset features and predict the result based on testing input. In this approach two different variants of machine learning principles are utilized to classify the resulting nature with better accuracy levels and cross-validations are providing best probabilistic results in outcome. And these two logics are integrated together to produce a new hybrid machine learning strategy to predict the rainfall status exactly and save human life against disasters. In this paper, a novel dataset is utilized from Regional Meteorological Centre Chennai to predict the rainfall summary in clear manner and the summarization of specific dataset is described on further sections. The proposed approach of INNM assures the resulting accuracy levels around 96.5% in prediction with lowest error ratio of 0.04% and the resulting portion of this paper provides a proper proof of this outcome in graphical manner.

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.

scholarly journals Result and Performance Analysis of Rainfall Prediction System Based on Deep Neural Network

2020 ◽  
pp. 633-638
Author(s):  
Akshay Rajendra Naik ◽  
A. V. Deorankar ◽  
P. B. Ambhore
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Neural Network ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Methods ◽  
Rainfall Prediction ◽  
Machine Learning Methods ◽  
Vector Machines ◽  
And Performance

Rainfall prediction is useful for all people for decision making in all fields, such as out door gamming, farming, traveling, and factory and for other activities. We studied various methods for rainfall prediction such as machine learning and neural networks. There is various machine learning algorithms are used in previous existing methods such as naïve byes, support vector machines, random forest, decision trees, and ensemble learning methods. We used deep neural network for rainfall prediction, and for optimization of deep neural network Adam optimizer is used for setting modal parameters, as a result our method gives better results as compare to other machine learning methods.

Machine Learning Techniques for Rainfall Prediction Using Neural Network

2019 ◽  
Vol 16 (8) ◽  
pp. 3319-3323 ◽  
Author(s):  
J. Refonaa ◽  
M. Lakshmi ◽  
S. Dhamodaran ◽  
Surya Teja ◽  
T. N. M. Pradeep
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Human Life ◽  
Research Area ◽  
Machine Learning Techniques ◽  
Rainfall Prediction ◽  
Learning Techniques ◽  
Fall Prediction ◽  
Efficient Prediction ◽  
Rain Fall

Rainfall prediction accuracy in meteorological department is still a major research area. Accuracy in prediction of rainfall may help in knowing heavy rainfall prior and preventing disasters. This mainly associated with economy and human life. There is necessary for efficient prediction system to identify drought and flood prior, so that people and government can get prepared for any disaster. Our country economy are mainly depends on agriculture, there is a great importance for rainfall prediction in India. Dynamics in atmosphere is the major cause for failure of existing statistical techniques for rainfall prediction. Taking these in consideration, we propose, Neural network based rain fall prediction for better showing better performance. We exploit machine learning, in which neural network model is used from Keras package available in Python. The objective of the work is to make this model more reliable for non-technical persons.

scholarly journals An Efficient Hybrid Machine Learning Classifier for Rainfall Prediction

2021 ◽  
Vol 1770 (1) ◽  
pp. 012012
Author(s):  
P. Asha ◽  
A. Jesudoss ◽  
S. Prince Mary ◽  
K. V. Sai Sandeep ◽  
K. Harsha Vardhan
Keyword(s):  
Machine Learning ◽  
Rainfall Prediction ◽  
Learning Classifier ◽  
Hybrid Machine

Internet financial supervision based on machine learning and improved neural network

2020 ◽  
pp. 1-12
Author(s):  
Cao Yanli
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Credit Risk ◽  
Machine Learning Algorithms ◽  
Financial Supervision ◽  
Loan Pricing ◽  
Comprehensive Management ◽  
Risk Pricing ◽  
Basel Agreement ◽  
Relevant Factors

The research on the risk pricing of Internet finance online loans not only enriches the theory and methods of online loan pricing, but also helps to improve the level of online loan risk pricing. In order to improve the efficiency of Internet financial supervision, this article builds an Internet financial supervision system based on machine learning algorithms and improved neural network algorithms. Moreover, on the basis of factor analysis and discretization of loan data, this paper selects the relatively mature Logistic regression model to evaluate the credit risk of the borrower and considers the comprehensive management of credit risk and the matching with income. In addition, according to the relevant provisions of the New Basel Agreement on expected losses and economic capital, starting from the relevant factors, this article combines the credit risk assessment results to obtain relevant factors through regional research and conduct empirical analysis. The research results show that the model constructed in this paper has certain reliability.

Application of stacking hybrid machine learning algorithms in delineating multi-type flooding in Bangladesh

2021 ◽  
Vol 295 ◽  
pp. 113086
Author(s):  
Mahfuzur Rahman ◽  
Ningsheng Chen ◽  
Ahmed Elbeltagi ◽  
Md Monirul Islam ◽  
Mehtab Alam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Hybrid Machine

EMOTIONS RECOGNITION IN HUMAN SPEECH USING DEEP NEURAL NETWORKS

2021 ◽  
pp. 44-51
Author(s):  
E. Yu. Shchetinin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Recurrent Neural Network ◽  
Deep Neural Networks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Audio Recordings ◽  
Computer Studies

The recognition of human emotions is one of the most relevant and dynamically developing areas of modern speech technologies, and the recognition of emotions in speech (RER) is the most demanded part of them. In this paper, we propose a computer model of emotion recognition based on an ensemble of bidirectional recurrent neural network with LSTM memory cell and deep convolutional neural network ResNet18. In this paper, computer studies of the RAVDESS database containing emotional speech of a person are carried out. RAVDESS-a data set containing 7356 files. Entries contain the following emotions: 0 – neutral, 1 – calm, 2 – happiness, 3 – sadness, 4 – anger, 5 – fear, 6 – disgust, 7 – surprise. In total, the database contains 16 classes (8 emotions divided into male and female) for a total of 1440 samples (speech only). To train machine learning algorithms and deep neural networks to recognize emotions, existing audio recordings must be pre-processed in such a way as to extract the main characteristic features of certain emotions. This was done using Mel-frequency cepstral coefficients, chroma coefficients, as well as the characteristics of the frequency spectrum of audio recordings. In this paper, computer studies of various models of neural networks for emotion recognition are carried out on the example of the data described above. In addition, machine learning algorithms were used for comparative analysis. Thus, the following models were trained during the experiments: logistic regression (LR), classifier based on the support vector machine (SVM), decision tree (DT), random forest (RF), gradient boosting over trees – XGBoost, convolutional neural network CNN, recurrent neural network RNN (ResNet18), as well as an ensemble of convolutional and recurrent networks Stacked CNN-RNN. The results show that neural networks showed much higher accuracy in recognizing and classifying emotions than the machine learning algorithms used. Of the three neural network models presented, the CNN + BLSTM ensemble showed higher accuracy.

Robust hybrid machine learning algorithms for gas flow rates prediction through wellhead chokes in gas condensate fields

Fuel ◽  
2022 ◽  
Vol 308 ◽  
pp. 121872
Author(s):  
Abouzar Rajabi Behesht Abad ◽  
Hamzeh Ghorbani ◽  
Nima Mohamadian ◽  
Shadfar Davoodi ◽  
Mohammad Mehrad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gas Flow ◽  
Learning Algorithms ◽  
Gas Condensate ◽  
Machine Learning Algorithms ◽  
Flow Rates ◽  
Hybrid Machine

Detection of Spoofing Attack using Machine Learning based on Multi-Layer Neural Network in Single-Frequency GPS Receivers

2017 ◽  
Vol 71 (1) ◽  
pp. 169-188 ◽  
Author(s):  
E. Shafiee ◽  
M. R. Mosavi ◽  
M. Moazedi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Late Phase ◽  
Machine Learning Algorithms ◽  
Computational Method ◽  
Single Frequency ◽  
Detection Accuracy ◽  
Gps Receiver ◽  
Navigation Data ◽  
Spoofing Detection

The importance of the Global Positioning System (GPS) and related electronic systems continues to increase in a range of environmental, engineering and navigation applications. However, civilian GPS signals are vulnerable to Radio Frequency (RF) interference. Spoofing is an intentional intervention that aims to force a GPS receiver to acquire and track invalid navigation data. Analysis of spoofing and authentic signal patterns represents the differences as phase, energy and imaginary components of the signal. In this paper, early-late phase, delta, and signal level as the three main features are extracted from the correlation output of the tracking loop. Using these features, spoofing detection can be performed by exploiting conventional machine learning algorithms such as K-Nearest Neighbourhood (KNN) and naive Bayesian classifier. A Neural Network (NN) as a learning machine is a modern computational method for collecting the required knowledge and predicting the output values in complicated systems. This paper presents a new approach for GPS spoofing detection based on multi-layer NN whose inputs are indices of features. Simulation results on a software GPS receiver showed adequate detection accuracy was obtained from NN with a short detection time.

scholarly journals Internet of Things-Based Intelligent Smart Home Control System

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Olutosin Taiwo ◽  
Absalom E. Ezugwu
Keyword(s):  
Machine Learning ◽  
Mobile Application ◽  
Smart Home ◽  
Human Life ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Automation System ◽  
Home Automation ◽  
Area Of Interest ◽  
Home Automation System

The smart home is now an established area of interest and research that contributes to comfort in modern homes. With the Internet being an essential part of broad communication in modern life, IoT has allowed homes to go beyond building to interactive abodes. In many spheres of human life, the IoT has grown exponentially, including monitoring ecological factors, controlling the home and its appliances, and storing data generated by devices in the house in the cloud. Smart home includes multiple components, technologies, and devices that generate valuable data for predicting home and environment activities. This work presents the design and development of a ubiquitous, cloud-based intelligent home automation system. The system controls, monitors, and oversees the security of a home and its environment via an Android mobile application. One module controls and monitors electrical appliances and environmental factors, while another module oversees the home’s security by detecting motion and capturing images. Our work uses a camera to capture images of objects triggered by their motion being detected. To avoid false alarms, we used the concept of machine learning to differentiate between images of regular home occupants and those of an intruder. The support vector machine algorithm is proposed in this study to classify the features of the image captured and determine if it is that of a regular home occupant or an intruder before sending an alarm to the user. The design of the mobile application allows a graphical display of the activities in the house. Our work proves that machine learning algorithms can improve home automation system functionality and enhance home security. The work’s prototype was implemented using an ESP8266 board, an ESP32-CAM board, a 5 V four-channel relay module, and sensors.

Sign in / Sign up

Export Citation Format

Share Document