A Review on Machine Learning and Deep Learning based Rainfall Prediction Methods

Srinu, Nidamanuri; Bindu, Bejawada Hima

doi:10.1109/icpects56089.2022.10047554

Cited by 5 publications

(4 citation statements)

References 19 publications

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“…In contrast to feed-forward neural networks, which analyze each input independently, RNNs contain a memory component that enables them to keep account of earlier inputs in the sequence. Therefore, they are ideally adapted for activities like rainfall forecasting, where the current prediction relies on earlier measurements [67].…”

Section: Recurrent Neural Network (Rnns)mentioning

confidence: 99%

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Potential of Artificial Intelligence-Based Techniques for Rainfall Forecasting in Thailand: A Comprehensive Review

Waqas,

Humphries,

Wangwongchai

et al. 2023

Water

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Rainfall forecasting is one of the most challenging factors of weather forecasting all over the planet. Due to climate change, Thailand has experienced extreme weather events, including prolonged lacks of and heavy rainfall. Accurate rainfall forecasting is crucial for Thailand’s agricultural sector. Agriculture depends on rainfall water, which is important for water resources, adversity management, and overall socio-economic development. Artificial intelligence techniques (AITs) have shown remarkable precision in rainfall forecasting in the past two decades. AITs may accurately forecast rainfall by identifying hidden patterns from past weather data features. This research investigates and reviews the most recent AITs focused on advanced machine learning (ML), artificial neural networks (ANNs), and deep learning (DL) utilized for rainfall forecasting. For this investigation, academic articles from credible online search libraries published between 2000 and 2022 are analyzed. The authors focus on Thailand and the worldwide applications of AITs for rainfall forecasting and determine the best methods for Thailand. This will assist academics in analyzing the most recent work on rainfall forecasting, with a particular emphasis on AITs, but it will also serve as a benchmark for future comparisons. The investigation concludes that hybrid models combining ANNs with wavelet transformation and bootstrapping can improve the current accuracy of rainfall forecasting in Thailand.

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Section: Recurrent Neural Network (Rnns)mentioning

confidence: 99%

“…Recent research has demonstrated that RNNs outperform conventional statistical approaches and FFNNs, indicating that they capture better complex non-linear correlations in the data. However, RNNs have disadvantages, such as the prospect of gradients vanishing or exploding and the need for expensive computation [67].…”

Section: Recurrent Neural Network (Rnns)mentioning

confidence: 99%

Potential of Artificial Intelligence-Based Techniques for Rainfall Forecasting in Thailand: A Comprehensive Review

Waqas,

Humphries,

Wangwongchai

et al. 2023

Water

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“…Various machine learning methodologies have been investigated in the context of rainfall prediction, focusing on diverse geographical regions, including South Africa, China, and other nations [6][7][8][9]. Various classifiers are employed for rainfall prediction, including Random Forest, Decision Tree, Support Vector Machine, K-nearest Neighbour (KNN), and Naïve Bayes [10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Rainfall and Daily Average Temperature Prediction using Machine Learning: A Case Study of Bangladesh

Hussain,

Tripura,

Aslam

2024

Preprint

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Heavy rains have created significant threats to human health and life. Floods and other natural disasters, which have a global impact annually, can be attributed to extended durations of intense precipitation. Accurate rainfall predictions are crucial in nations such as Bangladesh, where agriculture is the predominant occupation. The efficiency of machine learning (ML) methods is enhanced by the non-linearity of rainfall, surpassing the effectiveness of other alternative ways. Machine learning techniques show that individual classifiers exhibit worse accuracy than ensemble learning (EL) methodologies. Ensemble Learning techniques are utilized for rainfall prediction and estimating rainfall quantity and daily average temperature to enhance comprehension of the diverse Machine Learning algorithms. This research implements the machine learning techniques and ensemble-based classifier to predict the rainfall occurrence, along with the machine learning regressor models and ensemble-based regressor for the rainfall amount prediction and daily average temperature prediction, using Bangladesh Weather Dataset. The results of the machine learning and ensemble-based models are compared using the Accuracy and F1 score for rainfall occurrence prediction. In contrast, MAE and RMSE evaluation metrics are used for ensemble regressor and regression algorithms for the rainfall amount and daily average temperature prediction. With an accuracy of 83.41% and a recall of 78.17%, the Ensemble Classifier is the best at predicting when it will rain, but its precision of 51.16% stands in for the lowest. The Ensemble Regression model outperforms Linear Regression, Random Forest, and SVR in rainfall amount prediction, with the lowest MAE of 0.36 and RMSE of 0.90. The Ensemble Regression provides the most precise results for daily average temperature prediction with the lowest MAE 0.42 and RMSE 0.54 highlighting its superiority over the other regression models in forecasting temperature with less error. Ensemble approaches consistently lead task performance metrics.

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“…In this section, we begin with a brief review on selected works that have proposed benchmark forecasting techniques and hybrid models integrating wavelet analysis for the purpose of price forecasting. Extensive research has been conducted in the realm of time series forecasting, leading to the proposal and evaluation of numerous modeling techniques [8,9]. The autoregressive integrated moving average (ARIMA) methodology has emerged as the most widely employed linear technique in time series analysis [10].…”

Section: Introductionmentioning

confidence: 99%

Can Denoising Enhance Prediction Accuracy of Learning Models? A Case of Wavelet Decomposition Approach

Tamilselvi,

Yeasin,

Paul

et al. 2024

Forecasting

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Denoising is an integral part of the data pre-processing pipeline that often works in conjunction with model development for enhancing the quality of data, improving model accuracy, preventing overfitting, and contributing to the overall robustness of predictive models. Algorithms based on a combination of wavelet with deep learning, machine learning, and stochastic model have been proposed. The denoised series are fitted with various benchmark models, including long short-term memory (LSTM), support vector regression (SVR), artificial neural network (ANN), and autoregressive integrated moving average (ARIMA) models. The effectiveness of a wavelet-based denoising approach was investigated on monthly wholesale price data for three major spices (turmeric, coriander, and cumin) for various markets in India. The predictive performance of these models is assessed using root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE). The wavelet LSTM model with Haar filter at level 6 emerged as a robust choice for accurate price predictions across all spices. It was found that the wavelet LSTM model had a significant gain in accuracy than the LSTM model by more than 30% across all accuracy metrics. The results clearly highlighted the efficacy of a wavelet-based denoising approach in enhancing the accuracy of price forecasting.

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A Review on Machine Learning and Deep Learning based Rainfall Prediction Methods

Cited by 5 publications

References 19 publications

Potential of Artificial Intelligence-Based Techniques for Rainfall Forecasting in Thailand: A Comprehensive Review

Potential of Artificial Intelligence-Based Techniques for Rainfall Forecasting in Thailand: A Comprehensive Review

Rainfall and Daily Average Temperature Prediction using Machine Learning: A Case Study of Bangladesh

Can Denoising Enhance Prediction Accuracy of Learning Models? A Case of Wavelet Decomposition Approach

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