A Comparative Study of LGBM-SVR Hybrid Machine Learning Model for Rainfall Prediction

Maliyeckel, Merin Benny; Sai, B. Chaithanya; Naveen, J.

doi:10.1109/icccnt51525.2021.9579628

Cited by 5 publications

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Song et al [10] proposed a new machine-learning-based model for summer hourly precipitation forecasting in the Eastern Alps, demonstrating that machine learning methods are a promising approach for precipitation forecasting. Maliyeckel et al [11] used the LightGBM and SVM integrated model to make predictions using the preprocessed dataset. Compared with a single model, the root mean square error of the hybrid model was the smallest and the rainfall prediction results were more accurate.…”

Section: Introductionmentioning

confidence: 99%

RLNformer: A Rainfall Levels Nowcasting Model Based on Conv1D_Transformer for the Northern Xinjiang Area of China

Liu,

Chen

2023

Water

View full text Add to dashboard Cite

Accurate precipitation forecasting is of great significance to social life and economic activities. Due to the influence of various factors such as topography, climate, and altitude, the precipitation in semi-arid and arid areas shows the characteristics of large fluctuation, short duration, and low probability of occurrence. Therefore, it is highly challenging to accurately predict precipitation in the northern Xinjiang area of China, which is located in the semi-arid and arid climate region. In this study, six meteorological stations in the northern Xinjiang area were selected as the research area. Due to the high volatility of rainfall in this area, the rainfall was divided into four levels, namely, “no rain”, “light rain”, “moderate rain”, and “heavy rain and above”, for rainfall level prediction. In order to improve the prediction performance, this study proposed a rainfall levels nowcasting model based on Conv1D_Transformer (RLNformer). Firstly, the maximum information coefficient (MIC) method was used for feature selection and sliding the data, that is, the data of the first 24 h were used to predict the rainfall levels in the next 3 h. Then, the Conv1D layer was used to replace the word-embedding layer of the transformer, enabling it to extract the relationships between features of time series data and allowing multi-head attention to better capture contextual information in the input sequence. Additionally, a normalization layer was placed before the multi-head attention layer to ensure that the input data had an appropriate scale and normalization, thereby reducing the sensitivity of the model to the distribution of input data and helping to improve model performance. To verify the effectiveness and generalization of the proposed model, the same experiments were conducted on the Indian public dataset, and seven models were selected as benchmark models. Compared with the benchmark models, RLNformer achieved the highest accuracy on both datasets, which were 96.41% and 88.95%, respectively. It also had higher accuracy in the prediction of each category, especially the minority category, which has certain reference significance and practical value.

show abstract

Section: Introductionmentioning

confidence: 99%