Prediction of rainfall using improved deep learning with particle swarm optimization

Cholissodin, Imam; Sutrisno, Sutrisno

doi:10.12928/telkomnika.v18i5.14665

Cited by 12 publications

(10 citation statements)

References 19 publications

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“…If the time features such as month and hour are also set by the one-hot encoding method, a large feature space will be occupied. Moreover, the temporal continuity such as between December and January will be destroyed if the month feature (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12) is directly mapped into the interval of 0-1. In this work, clock projection is utilized to extract the temporal features.…”

Section: Additional Spatiotemporal Feathersmentioning

confidence: 99%

“…𝐶 , (𝑗) = sign 𝐶 , (𝑗) * 𝐶 , (𝑗) − 𝑇ℎ (𝑝) , 𝐶 , (𝑗) 𝑇ℎ (𝑝) 0 𝐶 , (𝑗) < 𝑇ℎ (𝑝) (11) where 𝑇ℎ (𝑝) is the wavelet packet threshold, combining with the mean and variance. 𝐶 , (𝑗), 𝑝 = 1, 2, … , 8 is the wavelet packet transform coefficients, and 𝑀 is the coefficient In addition, for the choice of the wavelet packet threshold, the three-layer wavelet packet and the soft-threshold is utilized.…”

Section: Wavelet Packet Denoising Principlementioning

confidence: 99%

“…It is well known that several machine-learning methods including artificial intelligence (AI) methods have been demonstrated to be powerful methods for weather forecasting [9][10][11][12][13]. For one-dimensional timeseries meteorological data, some data-driven machine-learning approaches have been presented for weather forecasting.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Machine-Learning Approach Combining Wavelet Packet Denoising with Catboost for Weather Forecasting

Niu

Zang

et al. 2021

Atmosphere

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Accurate forecasting of future meteorological elements is critical and has profoundly affected human life in many aspects from rainstorm warning to flight safety. The conventional numerical weather prediction (NWP) sometimes leads to unsatisfactory performance due to inappropriate initial state settings. In this paper, a short-term weather forecasting model based on wavelet packet denoising and Catboost is proposed, which takes advantage of the fusion information combining the historical observation data with the prior knowledge from NWP. The feature selection and spatiotemporal feather addition are also explored to further improve performance. The proposed method is evaluated on the datasets provided by Beijing weather stations. Experimental results demonstrate that compared with many deep-learning or machine-learning methods such as LSTM, Seq2Seq, and random forest, the proposed Catboost model incorporated with wavelet packet denoising can achieve shorter convergence time and higher prediction accuracy.

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Section: Additional Spatiotemporal Feathersmentioning

confidence: 99%

Section: Wavelet Packet Denoising Principlementioning

confidence: 99%

See 1 more Smart Citation

A Machine-Learning Approach Combining Wavelet Packet Denoising with Catboost for Weather Forecasting

Niu

Zang

et al. 2021

Atmosphere

View full text Add to dashboard Cite

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“…In [6]- [8] has developed a weather forecasting system for long-term prediction by utilizing artificial intelligence. Some institutions and companies even have their weather stations to collect weather data and predict upcoming weather events.…”

Section: Introductionmentioning

confidence: 99%

A real-time data association of internet of things based for expert weather station system

Indrabayu

Areni

Bustamin

et al. 2022

IJ-AI

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The wind carries moisture into an atmosphere and hot or cold air into a climate, affecting weather patterns. Knowing where the wind is coming from gives essential insight into what kind of temperatures are to be expected. However, the wind is affected by spatial and temporal variabilities, thus making it difficult to predict. This study focuses on finding data associations from the weather station installed at Hasanuddin University Campus based on internet of things (IoT) using Raspberry Pi as a gateway that associated all the meteorological data from sensors. The generation of association rules compares the Apriori and FP-growth algorithms to determine relations among itemsets. The results show that high humidity and warm temperature tend to associate with a westerly wind and occur at night. In contrast, conditions with less humid and moderate temperatures tend to have southerly and southeasterly wind.

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“…With the improvement of computing power, machine learning algorithms have boosted great interest in radar echo extrapolation [26][27][28][29], which is essentially a spatiotemporal sequence forecasting problem [30,31]. The sequences of past radar echo maps are input, and the future radar echo sequences are output.…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Spatiotemporal Context Refinement Network for Precipitation Nowcasting

et al. 2021

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Precipitation nowcasting by radar echo extrapolation using machine learning algorithms is a field worthy of further study, since rainfall prediction is essential in work and life. Current methods of predicting the radar echo images need further improvement in prediction accuracy as well as in presenting the predicted details of the radar echo images. In this paper, we propose a two-stage spatiotemporal context refinement network (2S-STRef) to predict future pixel-level radar echo maps (deterministic output) more accurately and with more distinct details. The first stage is an efficient and concise spatiotemporal prediction network, which uses the spatiotemporal RNN module embedded in an encoder and decoder structure to give a first-stage prediction. The second stage is a proposed detail refinement net, which can preserve the high-frequency detailed feature of the radar echo images by using the multi-scale feature extraction and fusion residual block. We used a real-world radar echo map dataset of South China to evaluate the proposed 2S-STRef model. The experiments showed that compared with the PredRNN++ and ConvLSTM method, our 2S-STRef model performs better on the precipitation nowcasting, as well as at the image quality evaluating index and the forecasting indices. At a given 45dBZ echo threshold (heavy precipitation) and with a 2 h lead time, the widely used CSI, HSS, and SSIM indices of the proposed 2S-STRef model are found equal to 0.195, 0.312, and 0.665, respectively. In this case, the proposed model outperforms the OpticalFlow method and PredRNN++ model.

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Prediction of rainfall using improved deep learning with particle swarm optimization

Cited by 12 publications

References 19 publications

A Machine-Learning Approach Combining Wavelet Packet Denoising with Catboost for Weather Forecasting

A Machine-Learning Approach Combining Wavelet Packet Denoising with Catboost for Weather Forecasting

A real-time data association of internet of things based for expert weather station system

Two-Stage Spatiotemporal Context Refinement Network for Precipitation Nowcasting

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