Artificial Neural Network for Rainfall Analysis Using Deep Learning Techniques

Nandakumar, S.; Valarmathi, R.; Juliet, P Sudha; Brindha, G.

doi:10.1088/1742-6596/1964/4/042022

Cited by 7 publications

(5 citation statements)

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“…and self-treatment. Network output varies depending on network connectivity, load speed and operating pressure [3][4][5]. The network itself can be either an algorithm or a function included in a given context and can be an indicator of a good idea.…”

Section: Literature Reviewmentioning

confidence: 99%

Quality Evaluation Methods of College English Teaching Reform Based on Artificial Neural Network

Yang

2024

jes

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This paper aims to enhance the effectiveness of intelligent evaluation in college English teaching and explore the role of artificial neural networks in evaluating college English teaching reform. A research study is conducted to develop an intelligent evaluation model by refining algorithm models and utilizing comparative research methods to address limitations of traditional algorithms. The neural network algorithms are improved through model construction techniques, with practical effectiveness assessed through simulation training methods. The algorithm model is applied to assess teaching quality in universities, with experimental research integrating questionnaire surveys and simulation data processing methods. The feasibility of the approach is verified through data evaluation. Simulation analysis demonstrates that the enhanced neural network algorithm reduces running time and enhances accuracy compared to traditional evaluation algorithms. Experimental studies indicate that employing artificial neural networks for evaluating the quality of college English teaching reform yields positive outcomes, aiding students and teachers in understanding their situations within mobile computing environments. This study confirms that combining a neural network model with data survey methods effectively meets the requirements of contemporary efficient teaching quality evaluation. The innovative integration of intelligent algorithms, factor analysis, surveys, and statistical methods enhances the progressive nature of teaching quality evaluation.

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Section: Literature Reviewmentioning

confidence: 99%

Quality Evaluation Methods of College English Teaching Reform Based on Artificial Neural Network

Yang

2024

jes

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“…Following studies by [52,53,59], which used water vapor in the atmosphere and soil moisture as ancillary variables, this study merged two models based on an MLR framework, as defined below:…”

Section: Multivariant Linear Regression Modelmentioning

confidence: 99%

“…Some of these studies have evaluated IMERG data on a global scale (e.g., [6]), while others have focused on some specific areas of the globe such as Europe [7][8][9], vast territories throughout Asia [10][11][12][13][14][15], and America [16][17][18][19]. Their investigations suggest the promising use of IMERG to estimate Learning (ML) techniques, such as ANNs, have been increasingly applied in rainfall studies due to their ability to extract a non-linear relationship between independent and dependent variables, without the need for predefined relationships; this has led to a significant improvement compared to traditional blending methods [46,49,[52][53][54][55]. A summary of selected literature reviews in this field is reported in Table 1.…”

Section: Introductionmentioning

confidence: 99%

Smart Data Blending Framework to Enhance Precipitation Estimation through Interconnected Atmospheric, Satellite, and Surface Variables

2023

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Accurate precipitation estimation remains a challenge, though it is fundamental for most hydrological analyses. In this regard, this study aims to achieve two objectives. Firstly, we evaluate the performance of two precipitation products from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM-IMERG) for Sicily, Italy, from 2016 to 2020 by a set of categorical indicators and statistical indices. Analyses indicate the favorable performance of daily estimates, while half-hourly estimates exhibited poorer performance, revealing larger discrepancies between satellite and ground-based measurements at sub-hourly timescales. Secondly, we propose four multi-source merged models within Artificial Neural Network (ANN) and Multivariant Linear Regression (MLR) blending frameworks to seek potential improvement by exploiting different combinations of Soil Moisture (SM) measurements from the Soil Moisture Active Passive (SMAP) mission and atmospheric factor of Precipitable Water Vapor (PWV) estimations, from the Advanced Microwave Scanning Radiometer-2 (AMSR2). Spatial distribution maps of some diagnostic indices used to quantitatively evaluate the quality of models reveal the best performance of ANNs over the entire domain. Assessing variable sensitivity reveals the importance of IMERG satellite precipitation and PWV in non-linear models such as ANNs, which outperform the MLR modeling framework and individual IMERG products.

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“…These studies concluded that many studies have used ANN in predicting future weather and also concludes that data mining techniques, especially ANN can be used to predict weather conditions accurately. [5] states that some researchers use ANN for rainfall prediction, because it is a valid and more accurate method than conventional mathematics or numerical approaches. This paper discusses the comparison of the predicted perceived value with BPN, RBFN, SVM.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Because the factor of sufficient water availability will support agricultural productivity. So it is necessary to involve science and technology to determine weather conditions [5].…”

Section: Introductionmentioning

confidence: 99%

The Effect of Number of Factors and Data on Monthly Weather Classification Performance Using Artificial Neural Networks

Shofura

Suryani

Salma

et al. 2021

ijoict

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Current weather-related research only focuses on weather prediction based on raw data and the factors used are generally 4 factors: average temperature, solar radiation, air pressure, and wind. In this research, monthly weather prediction is done using 5 factors where the additional factor used is rainfall in the previous time. In contrast to previous prediction research, the prediction process carried out in this study emphasizes the modeling of training data according to the desired prediction model.. These two things distinguish this research from previous studies. The prediction model used in this study is a classification-based prediction model that is the Artificial Neural Network (ANN) method combined with the backpropagation algorithm for calculating the weight of the ANN network. The data used are meteorological data from 2010 to 2018 in the Bogor area, where data from 2010 to 2016 are used as training data, and data from 2017 to 2018 are used as test data. The results of this study indicate that the design of the model with the use of data for 6 years with feature data of 5 factors has an accuracy rate of 83.33%.

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Artificial Neural Network for Rainfall Analysis Using Deep Learning Techniques

Cited by 7 publications

References 1 publication

Quality Evaluation Methods of College English Teaching Reform Based on Artificial Neural Network

Quality Evaluation Methods of College English Teaching Reform Based on Artificial Neural Network

Smart Data Blending Framework to Enhance Precipitation Estimation through Interconnected Atmospheric, Satellite, and Surface Variables

The Effect of Number of Factors and Data on Monthly Weather Classification Performance Using Artificial Neural Networks

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