Study of short term rain forecasting using machine learning based approach

Balamurugan, M.; Manojkumar, R.

doi:10.1007/s11276-019-02168-3

Cited by 24 publications

(18 citation statements)

References 24 publications

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“…The above discussion clearly highlights the need for alternate methods of short-term rainfall forecasting that are versatile and, at the same time, provide high efficiency. In the recent past, researchers have attempted to resolve some of the drawbacks of statistical and physically based models by using the capabilities of ML approaches or hybrid models (Hong 2008;Sumi et al 2012;Cramer et al 2017;Balamurugan & Manojkumar 2021;Ridwan et al 2021). Artificial neural network (ANN), k-nearest neighbor (KNN), SVM, decision tree (DT), and RF are some of the popular ML models that were employed to handle complex nonlinear association (Hong & Pai 2007;Hong 2008;Sumi et al 2012;Akrami et al 2014;Cramer et al 2017;Abbot & Marohasy 2018).…”

Section: Methodsmentioning

confidence: 99%

A systematic quantitative review on the performance of some of the recent short-term rainfall forecasting techniques

Shejule

Pekkat

2022

Journal of Water and Climate Change

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Rainfall forecasting is a high-priority research problem due to the complex interplay of multiple factors. Despite extensive studies, a systematic quantitative review of recent developments in rainfall forecasting is lacking in the literature. This study conducted a systematic quantitative review of statistical, numerical weather prediction (NWP) and machine learning (ML) techniques for rainfall forecasting. The review adopted the preferred reporting items for systematic reviews and meta-analyses (PRISMA) technique for screening keywords and abstracts, leading to 110 qualified papers from multiple databases. The impact of rainfall threshold, meteorological parameters, topography, algorithm techniques, geographic location, the horizontal resolution of the model, and lead time on rainfall forecast was examined. The review shows the importance of precipitable water vapor (PWV) along with other meteorological parameters for accurate nowcasting in coastal and mountainous regions. An increase in rainfall forecast uncertainty with an increase in the lead time makes the NWP model less popular for the short-term forecast. The pre-processing techniques increased the accuracy of ML techniques by considering extreme values and detecting the irregularly distributed multi-scale features of rainfall in space and time. Future research can focus on hybrid models with improved accuracy for nowcasting. The output from the hybrid model serves as input for the decision support system required for urban flood risk management.

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Section: Methodsmentioning

confidence: 99%

A systematic quantitative review on the performance of some of the recent short-term rainfall forecasting techniques

Shejule

Pekkat

2022

Journal of Water and Climate Change

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“…Prakiraan cuaca adalah proses dari pengumpulan data dari kondisi atmosfer, yang terdiri dari temperatur, kelembaban, penyinaran matahari, dan kecepatan angin [1]. Cuaca mempengaruhi kehidupan manusia di berbagai aspek [2], [3].Faktor-faktor tersebut kemudian diteliti dan dicocokkan dengan cuaca hari, bulan, bahkan tahun sebelumnya sehingga mendapatkan perkiraan cuaca yang paling akurat [1]. Proses meneliti dan mencocokkan data dalam jumlah yang besar ini termasuk dalam cabang ilmu komputer yaitu data mining [4].…”

Section: Pendahuluanunclassified

Weather Forecast From Time Series Data Using LSTM Algorithm

Nugraha

Ariawan

Arifin

2023

JTIKP

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Accurate weather forecasts play an important role in today's world as various sectors such as marine, navigation, agriculture and industry are basically dependent on weather conditions. Weather forecasts are also used to predict the occurrence of natural disasters. Weather forecasting determines the exact value of weather parameters and then predicts future weather conditions. In this study the parameters used are. Different weather parameters were collected from the Serang Maritime Meteorological Station and then analyzed using a neural network-based algorithm, namely Long-short term memory (LSTM). In predicting future weather conditions using LSTM neural networks are trained using a combination of different weather parameters, the weather parameters used are temperature, humidity, rainfall, and wind speed. After training the LSTM model using these parameters, future weather predictions are performed. The prediction results are then evaluated using RMSE. Prediction results show that the model is more accurate when predicting temperature data with RMSE 0.37, then RMSE wind speed 0.72, RMSE sunlight 2.79, and RMSE humidity 5.05. This means that the model is very good at studying weather data, inversely proportional to humidity data.

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“…For this reason, CNNs and ConvLSTMs are mainly applied to data sets with short time intervals of no more than a few minutes between data points, which are typically much larger than data sets with longer time intervals [29][30][31][32][33][34][35][36][37][38][39][40]. For single-output prediction, a wider range of ML tools and time frames have been used, from linear methods in [17,21,41,42], to ensemble methods in [43][44][45], to hybrid methods in [28,[46][47][48], to deep models in [49][50][51][52][53][54][55][56] covering time scales from minutes to years.…”

Section: Literature Review and Scope Of The Researchmentioning

confidence: 99%

Basic Statistical Estimation Outperforms Machine Learning in Monthly Prediction of Seasonal Climatic Parameters

et al. 2021

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Machine learning (ML) has been utilized to predict climatic parameters, and many successes have been reported in the literature. In this paper, we scrutinize the effectiveness of five widely used ML algorithms in the monthly prediction of seasonal climatic parameters using monthly image data. Specifically, we quantify the predictive performance of these algorithms applied to five climatic parameters using various combinations of features. We compare the predictive accuracy of the resulting trained ML models to that of basic statistical estimators that are computed directly from the training data. Our results show that ML never significantly outperforms the statistical baseline, and underperforms for most feature sets. Unlike previous similar studies, we provide error bars for the relative performance of different predictors based on jackknife estimates applied to differences in predictive error magnitudes. We also show that the practice of shuffling data sequences which was employed in some previous references leads to data leakage, resulting in over-estimated performance. Ultimately, the paper demonstrates the importance of using well-grounded statistical techniques when producing and analyzing the results of ML predictive models.

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Study of short term rain forecasting using machine learning based approach

Cited by 24 publications

References 24 publications

A systematic quantitative review on the performance of some of the recent short-term rainfall forecasting techniques

A systematic quantitative review on the performance of some of the recent short-term rainfall forecasting techniques

Weather Forecast From Time Series Data Using LSTM Algorithm

Basic Statistical Estimation Outperforms Machine Learning in Monthly Prediction of Seasonal Climatic Parameters

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