Rainfall Analysis and Forecasting Using Deep Learning Technique

Kanchan, Pragati; Shardoor, Nikhil Kumar

doi:10.54060/jieee/002.02.015

Cited by 8 publications

(4 citation statements)

References 22 publications

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“…Their proposed deep and wide ANN outperforms the CNN. In another comparison of ANNs, Kanchan and Shardoor (2021) trained ANNs, RNNs, and LSTMs for Karnataka, India. They showed that LSTMs performed the best.…”

Section: D Rainfall Forecastingmentioning

confidence: 99%

A Systematic Review of Deep Learning Applications in Interpolation and Extrapolation of Precipitation Data

Sit¹,

Demiray²,

Demir³

2022

Preprint

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With technological enhancements, the volume, velocity, and variety (3Vs) of the raw digital Earth data have increased in recent years. Due to the increased availability of computer resources and the growing popularity of deep learning applications, this data has been a crucial source for data-driven studies that have transformed the fields of climate and earth science. One of the critical data sources is precipitation supporting climate and earth science studies on modeling, forecasting, and preparedness for extreme events (i.e., floods, droughts, pollution transport). In this study, we worked on an extensive review of manuscripts focusing on use of deep learning methods to tackle challenges either to improve the quality or extrapolate (forecast) rainfall datasets. The purpose of this study is to summarize the most recent developments in deep learning approaches for forecasting rainfall or improving rainfall datasets, as well as highlighting issues, shortcomings, and open questions with insightful recommendations for future directions.

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Section: D Rainfall Forecastingmentioning

confidence: 99%

A Systematic Review of Deep Learning Applications in Interpolation and Extrapolation of Precipitation Data

Sit¹,

Demiray²,

Demir³

2022

Preprint

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“…The F1-Score uses a precision score and a classifier recall score. F1-Score can be projected using the below formula (5).…”

Section: Model Evaluationmentioning

confidence: 99%

“…Historical data from weather and climate is collected and analyzed to make it predictable. Data such as temperature, humidity, air pressure, evaporation, sunlight, and rainfall levels are used to predict rainfall [5,6].…”

Section: Introductionmentioning

confidence: 99%

“…It demonstrates that ANN worked better for the M1 model (3-25-1) than the M2 model (3-50-1), indicating that the ANN approach performed better for one month than two months ahead. In addition to predicting rainfall using ANN, there is also a similar study as in [5], using the LSTM method for predicting monthly rainfall over the Karnataka subdivision. The results show that the LSTM optimized deep Several other studies have also used an interpolation algorithm approach such as Ordinary Kriging.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Rainfall Classification of Java Island with ANN-Feature Expansion and Ordinary Kriging

Maulana

Prasetiyowati²,

Sibaroni³

2022

mib

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Precipitation is one of the most important climatic variables in many aspects of our daily lives. High rainfall intensity can cause floods, landslides, and other natural disasters. Therefore, rainfall prediction is important for predicting natural disasters, assisting farmers in production decisions, and crop harvesting. In this research, a system is built to create a rainfall prediction map using a machine learning approach and spatial interpolation algorithms in Java, Indonesia. In the field of weather prediction, the artificial neural network approach is a popular machine learning method. The artificial neural network (ANN) method is a method that has the advantage of studying connections in the previously unknown hidden layer between input data and output data through training procedures. By using the ANN method, historical weather and climate data can be applied to create a classification model and predict rainfall classes. The classification of data is determined based on the attributes of historical weather and climate data, namely temperature, humidity, air pressure, evaporation, sunlight, and the level of rainfall in the time range per day and month. From the results of the ANN modeling, it was found that the 5C month model with an accuracy value of 89% as the best monthly ANN model, and the 6C day model with an accuracy value of 81% as the best daily ANN model. After going through ANN modeling, there is a spatial interpolation algorithm that is often used to estimate rainfall, namely Ordinary Kriging. The Ordinary Kriging approach is used to reduce the estimated variance and estimate the rainfall value in the case study area. After going through Ordinary Kriging modeling, a rainfall prediction map for the next six months and seven days is made based on the coordinates as a result of the research. The results of this research are rainfall prediction maps for the next six months and the next seven days on Java Island.

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Different Time-Increment Rainfall Prediction Models: a Machine Learning Approach Using Various Input Scenarios

Rahimi,

Yashooa,

Ahmed

et al. 2024

Water Resour Manage

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Rainfall Analysis and Forecasting Using Deep Learning Technique

Cited by 8 publications

References 22 publications

A Systematic Review of Deep Learning Applications in Interpolation and Extrapolation of Precipitation Data

A Systematic Review of Deep Learning Applications in Interpolation and Extrapolation of Precipitation Data

Prediction of Rainfall Classification of Java Island with ANN-Feature Expansion and Ordinary Kriging

Different Time-Increment Rainfall Prediction Models: a Machine Learning Approach Using Various Input Scenarios

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