Short-term rainfall forecasting using machine learning-based approaches of PSO-SVR, LSTM and CNN

Adaryani, Fatemeh rezaie; Mousavi, S. Jamshid; Jafari, Fatemeh

doi:10.1016/j.jhydrol.2022.128463

Cited by 83 publications

(35 citation statements)

References 44 publications

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“…To avoid local optimization, the particle swarm optimization (PSO) algorithm was employed in this study to optimize the main parameters of the SVM (penalty parameter (c) and kernel function parameter (g)) to improve the model performance. PSO originated from the study of the foraging behavior of bird flocks as a means to find the optimal destination for the flock through collective information sharing 32 …”

Section: Methodsmentioning

confidence: 99%

Detection of wheat saccharification power and protein content using stacked models integrated with hyperspectral imaging

Huang,

Tian,

Yang

et al. 2024

J Sci Food Agric

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BACKGROUNDWheat is one of the key ingredients used to make Chinese liquor, and its saccharification power and protein content directly affect the quality of the liquor. In pursuit of a non‐destructive assessment of wheat components and the optimization of raw material proportions in liquor, this study introduces a precise predictive model that integrates hyperspectral imaging (HSI) with stacked ensemble learning (SEL).RESULTSThis study extracted hyperspectral information from 14 different varieties of wheat and employed various algorithms for preprocessing. It was observed that multiplicative scatter correction (MSC) emerged as the most effective spectral preprocessing method. The feature wavelengths were extracted from the preprocessed spectral data using three different feature extraction methods. Then, single models (support vector machine (SVM), backpropagation neural network (BPNN), random forest (RF), and gradient boosting tree (XGBoost)) and a SEL model were developed to compare the prediction accuracies of the SEL model and the single models based on the full‐band spectral data and the characteristic wavelengths. The findings indicate that the MSC–competitive adaptive reweighted sampling–SEL model demonstrated the highest prediction accuracy, with Rp2 (test set‐determined coefficient) values of 0.9308 and 0.9939 for predicting the saccharification power and protein content and root mean square error of the test set values of 0.0081 U and 0.0116 g kg−1, respectively.CONCLUSIONThe predictive model established in this study, integrating HSI and SEL models, accurately detected wheat saccharification power and protein content. This validation underscores the practical potential of the SEL model and holds significant importance for non‐destructive component analysis of raw materials used in liquor. © 2024 Society of Chemical Industry.

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Section: Methodsmentioning

confidence: 99%

Detection of wheat saccharification power and protein content using stacked models integrated with hyperspectral imaging

Huang,

Tian,

Yang

et al. 2024

J Sci Food Agric

View full text Add to dashboard Cite

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“…In order to further improve the accuracy of the prediction model, PSO is used to optimize the SVR algorithm [ 14 ]. PSO is an optimization algorithm based on “population” and “evolution”.…”

Section: Methodsmentioning

confidence: 99%

Detection of Dihydrocoumarin in Coconut Juice via Photoelectric Detection System Based on Ultraviolet Absorption Spectrometry

Wang

et al. 2022

Sensors

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Based on ultraviolet absorption spectroscopy technology combined with stoichiometry, a portable photoelectric detection system with wireless transmission was designed with the advantages of simple operation, low cost, and quick response to realize the non-destructive detection of dihydrocoumarin content in coconut juice. Through the detection of a sample solution, the light intensity through the solution is measured and converted into absorbance. Particle swarm optimization (PSO) is applied to optimize support vector regression (SVR) to establish a corresponding concentration prediction model. At the same time, in order to solve the shortcomings of the conventional portable photoelectric detection equipment in data storage, data transmission, and other aspects, based on the optimal PSO-SVR model, we used Python language to develop a friendly graphical user interface (GUI), integrating data collection, storage, analysis, and prediction modeling in one, greatly simplifying the operation process. The experimental results show that, compared with the traditional methods, the system achieves the purpose of rapid and non-destructive detection and has a small gap compared with the detection results of the ultraviolet spectrophotometer. It provides a good method for the determination of dihydrocoumarin in coconut juice.

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“…Accurate rainfall prediction is vital in daily life, risk assessment, natural disaster prevention, and water resource planning and management (Ni et al 2020). The dynamic complexity and nonstationarity of measured hydrological data create signi cant challenges regarding rainfall prediction (Adaryani et al 2022). Models used for hydrometeorological time series prediction can be divided into physical process-driven models and data-driven models (He et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Rainfall time series prediction based on the DWT-SVR- Prophet hybrid model

Rao

et al. 2023

Preprint

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The discrete wavelet method can be used to decompose rainfall time series into subseries of different frequencies. It would be worthwhile to investigate whether combining forecasting results from different frequency subseries could improve the accuracy of rainfall prediction. A novel DWT-SVR-Prophet (DSP) hybrid model for rainfall prediction is proposed in this paper. First, the rainfall time series is decomposed into high-frequency and low-frequency subseries using discrete wavelet transform (DWT). The SVR and Prophet models are then used to predict high-frequency and low-frequency subsequences, respectively. Finally, the predicted rainfall is determined by summing the predicted values of each subsequence. A case study in China is conducted from January 1, 2014, to June 30, 2016. The results show that the DSP model provides excellent prediction, with RMSE, MAE, R2 values of 6.17, 3.3, and 0.75, respectively. The DSP model yields higher prediction accuracy than the three baseline models considered, with the prediction accuracy ranking as follows: DSP > SSP > Prophet > SVR. In addition, the DSP model is quite stable, and can achieve good results when applied to rainfall data from various climate types, with RMSEs ranging from 1.24 to 7.31, MAEs ranging from 0.52 to 6.14 and R2 values ranging from 0.62 to 0.75. The proposed model may provide a noval approach for rainfall forecasting and is readily adaptable to other time series predictions.

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Short-term rainfall forecasting using machine learning-based approaches of PSO-SVR, LSTM and CNN

Cited by 83 publications

References 44 publications

Detection of wheat saccharification power and protein content using stacked models integrated with hyperspectral imaging

Detection of wheat saccharification power and protein content using stacked models integrated with hyperspectral imaging

Detection of Dihydrocoumarin in Coconut Juice via Photoelectric Detection System Based on Ultraviolet Absorption Spectrometry

Rainfall time series prediction based on the DWT-SVR- Prophet hybrid model

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