Main Article Content
Rainfall is one of the key important factors affecting human life. Accurate precipitation forecasting allows humans to better prepare for various activities that will happen in the future. However, in some situations, the availability of weather data is limited, making it difficult to make accurate forecasting. Currently, much research has chosen deep neural networks as an algorithm to train forecasting models. The main idea is to come up with relevant features at the architecture level. Based on this paradigm, it has been shown that the appropriate deep neural network architecture can flexibly mix and match features that are relevant in predictions. Consequently, most of the existing research then focuses on some of the techniques to improve the performance of the models without paying much attention on the issues of adding relevant features. However, when the training data is limited, deep neural networks might not scale very well, thus making it difficult to mix and match features for predictions. This imposes a research question of how effective the proposed forecasting models might be when not adding relevant features to the models. The aims of this research are to develop and compare the efficiency of various models for short-term precipitation forecasting with and without adding relevant features. The experiment consists of 2 parts: (1) The experiment by exploring whether the models with relevant featured provided can achieve higher accuracy compared to original models in equivalent environments, and (2) The experiment by comparing accuracy between 5 models (ARIMA, ARIMAX, RNN, LSTM and GRU). The weather dataset, which is very limited in quantity, used in this research was collected from Suvarnabhumi Airport. The results show that adding relevant features can enhance the forecasting performance of the model when the weather data is limited. In addition, the GRU model with relevant featured provided is the most effective prediction.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
I/we certify that I/we have participated sufficiently in the intellectual content, conception and design of this work or the analysis and interpretation of the data (when applicable), as well as the writing of the manuscript, to take public responsibility for it and have agreed to have my/our name listed as a contributor. I/we believe the manuscript represents valid work. Neither this manuscript nor one with substantially similar content under my/our authorship has been published or is being considered for publication elsewhere, except as described in the covering letter. I/we certify that all the data collected during the study is presented in this manuscript and no data from the study has been or will be published separately. I/we attest that, if requested by the editors, I/we will provide the data/information or will cooperate fully in obtaining and providing the data/information on which the manuscript is based, for examination by the editors or their assignees. Financial interests, direct or indirect, that exist or may be perceived to exist for individual contributors in connection with the content of this paper have been disclosed in the cover letter. Sources of outside support of the project are named in the cover letter.
I/We hereby transfer(s), assign(s), or otherwise convey(s) all copyright ownership, including any and all rights incidental thereto, exclusively to the Journal, in the event that such work is published by the Journal. The Journal shall own the work, including 1) copyright; 2) the right to grant permission to republish the article in whole or in part, with or without fee; 3) the right to produce preprints or reprints and translate into languages other than English for sale or free distribution; and 4) the right to republish the work in a collection of articles in any other mechanical or electronic format.
We give the rights to the corresponding author to make necessary changes as per the request of the journal, do the rest of the correspondence on our behalf and he/she will act as the guarantor for the manuscript on our behalf.
All persons who have made substantial contributions to the work reported in the manuscript, but who are not contributors, are named in the Acknowledgment and have given me/us their written permission to be named. If I/we do not include an Acknowledgment that means I/we have not received substantial contributions from non-contributors and no contributor has been omitted.
Aurnhammer, C., and Frank, S. L, “Comparing gated and simple recurrent neural network architectures as models of human sentence processing”, 2019.
Chawla, N. V., Bowyer K. W., Hall, L. O., and Kegelmeyer W. P., “SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research”, vol. 16, pp. 321-357, 2002.
Chung, J., Gulcehre, C., Cho, K., and Bengio, Y., “Empirical evaluation of gated recurrent neural networks on sequence modeling”, 2014.
Fan, H., Jiang, M., Xu, L., Zhu, H., Cheng, J., and Jiang, J., “Comparison of long short-term memory networks and the hydrological model in runoff simulation,” Water, vol. 12 no. 1, pp. 175, 2020.
Hernández, E., Sanchez-Anguix, V., Julian, V., Palanca, J., and Duque, N., “Rainfall prediction: A deep learning approach. International Conference on Hybrid Artificial Intelligence Systems,” 2016.
Hochreiter, S., and Schmidhuber, J., “Long short-term memory. Neural Comput,” vol. 9, no.8, pp. 1735-1780, 1997.
Hung, N. Q., Babel, M. S., Weesakul, S., and Tripathi, N., “An artificial neural network model for rainfall forecasting in Bangkok, Thailand,” Hydrology and Earth System Sciences, vol.13(8), pp. 1413-1425, 2009.
Jalalkamali, A., Moradi, M., and Moradi, N., “Application of several artificial intelligence models and ARIMAX model for forecasting drought using the Standardized Precipitation Index,” International journal of environmental science and technology, vol.12(4), pp. 1201-1210, 2015.
Manokij, F., “Thailand's Precipitation Forecasting Using Deep Learning Approach,” Chulalongkorn University, Bangkok, 2019.
Narayanan, P., Basistha, A., Sarkar, S., and Sachdeva, K., “Trend analysis and ARIMA modelling of pre-monsoon rainfall data for western India,” Comptes Rendus Geoscience, vol. 345(1), pp. 22-27, 2013.
Poornima, S., and Pushpalatha, M., “Prediction of rainfall using intensified LSTM based recurrent neural network with weighted linear units,” Atmosphere, vol.10(11), pp. 668, 2019.
Salman, A. G., Heryadi, Y., Abdurahman, E., and Suparta, W., “Weather Forecasting Using Merged Long Short-Term Memory Model (LSTM) and Autoregressive Integrated Moving Average (ARIMA) Model,” J. Comput. Sci., vol. 14(7), pp. 930-938, 2018.
Sanguansat, P., “Artificial Intelligence with Machine Learning,” IDC Premier Limited, 2019.
Shewalkar, A., “Performance evaluation of deep neural networks applied to speech recognition: RNN, LSTM and GRU,” Journal of Artificial Intelligence and Soft Computing Research, vol. 9(4), pp. 235-245, 2019.
Srachoom, C., “Application of Artificial Neural Network for Weather Forecast,” Chiang Mai University. Chiang Mai, 2007.
Sukawat, D., “Weather forecast Knowledge,” [Online], Available: https://www.tmd.go.th/info/info.php?FileID=1.
Wang, S. W., Feng, J., and Liu, G., “Application of seasonal time series model in the precipitation forecast,” Mathematical and Computer Modelling, vol. 58(3-4), pp. 677-683, 2013.
Wangdi, K., Singhasivanon, P., Silawan, T., Lawpoolsri, S., White, N. J., and Kaewkungwal, J., “Development of temporal modelling for forecasting and prediction of malaria infections using time-series and ARIMAX analyses: a case study in endemic districts of Bhutan,” Malaria Journal, vol. 9(1), pp. 1-9, 2010.
Yang, S., Yu, X., and Zhou, Y., “LSTM and GRU neural network performance comparison study: Taking Yelp review dataset as an example,” 2020 International workshop on electronic communication and artificial intelligence (IWECAI), 2020.