Daily Rainfall Forecasting Using Meteorology Data with Long Short-Term Memory (LSTM) Network

Subject Areas : Environmental Management

Soo See Chai ¹ , Kok Luong Goh ²

1 - Faculty of Computer Science and Information Technology, University of Malaysia Sarawak (UNIMAS), 94300, Kota Samarahan, Sarawak, Malaysia
2 - International College of Advanced Technology Sarawak (i-CATS), Kuching, Sarawak, Malaysia

Received: 2021-09-25 Accepted : 2021-11-18 Published : 2022-06-01

Keywords:

References:

Kumar, D. , Singh, A., Samui, P. & Jha, R K..(2019), "Forecasting monthly precipitation using sequential modelling," Hydrological sciences journal, 64( 6),  690-700 .

Hernández, E. Sanchez-Anguix,V. Julian, V. Palanca, J. & Duque, N. (2016) "Rainfall Prediction: A Deep Learning Approach," Cham,Springer International Publishing, in Hybrid Artificial Intelligent Systems, 151-162.

Tran Anh,  D., Duc Dang, T. &. Pham Van, S. (2019) "Improved Rainfall Prediction Using Combined Pre-Processing Methods and Feed-Forward Neural Networks," J — Multidisciplinary Scientific Journal, 2,(1), 65-83, [Online]. Available: https://www.mdpi.com/2571-8800/2/1/6.

Kashiwao, Nakayama, Ando, T. K.,   S. K. M. Lee, & Bahadori, A. (2017) "A neural network-based local rainfall prediction system using meteorological data on the Internet: A case study using data from the Japan Meteorological Agency," Applied Soft Computing, 56, 317-330.

Liu,Q., Zou,Y. Liu, X. & Linge, N. (2019) "A survey on rainfall forecasting using artificial neural network," International Journal of Embedded Systems,11,(2), 240-249.

Darji, M. P., Dabhi,V. K. & Prajapati, H. B. (2015)"Rainfall forecasting using neural network: A survey," in 2015 International Conference on Advances in Computer Engineering and Applications,  IEEE, 706-713.

Mandal, T. & Jothiprakash, V. (2012) "Short-term rainfall prediction using ANN and MT techniques," ISH Journal of Hydraulic Engineering, 18(1), 20-26.

Solomatine, D. P. (2006)"Data‐driven modeling and computational intelligence methods in hydrology," Encyclopedia of hydrological sciences.

Pouyanfar S. & et al.,(2018) "A survey on deep learning: Algorithms, techniques, and applications," ACM Computing Surveys (CSUR), 51(5), 1-36, 2018.

Jia, Y., Wu, J., Ben-Akiva, Seshadri, M. R. & Du, Y. (2017) "Rainfall-integrated traffic speed prediction using deep learning method," IET Intelligent Transport Systems, 11(9), 531-536, 2017.

Nakisa, B., Rastgoo, M. N., A. Rakotonirainy, F. Maire, & Chandran,V. (2018) "Long short term memory hyperparameter optimization for a neural network based emotion recognition framework," IEEE Access,6, 49325-49338.

Akbari Asanjan, A., Yang,T. Hsu,K. Sorooshian,S., Lin, J. & Peng, Q. (2018) "Short‐term precipitation forecast based on the PERSIANN system and LSTM recurrent neural networks," Journal of Geophysical Research: Atmospheres,123(22), 12,543-12,563.

Kumar, A. Islam, T. Sekimoto, Y. Mattmann, C. & Wilson, B. (2020) "Convcast: An embedded convolutional LSTM based architecture for precipitation nowcasting using satellite data," Plos one,15(3), e0230114.

Salman, A. Heryadi, G., Abdurahman, Y. E. & Suparta,W. (2018) "Single layer & multi-layer long short-term memory (LSTM) model with intermediate variables for weather forecasting," Procedia Computer Science,  135, 89-98.

Hochreiter, S. & Schmidhuber, J.,(1997) "LSTM can solve hard long time lag problems," in Advances in neural information processing systems, 473-479.

Bengio,Y., Simard,P. & Frasconi,P., (1994) "Learning long-term dependencies with gradient descent is difficult," IEEE transactions on neural networks, 5(2),157-166.

Hochreiter, S.(1998) "The vanishing gradient problem during learning recurrent neural nets and problem solutions," International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 6,(2), 107-116.

Chen,Y., Liu, He,.S., Liu, S. K. & Zhao, J.  (2016)"Event extraction via bidirectional long short-term memory tensor neural networks," in Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data: Springer,  190-203.

Sakinah, N., Tahir, Badriyah, M. T. & Syarif, I. (2019) "LSTM With Adam Optimization-Powered High Accuracy Preeclampsia Classification," in 2019 International Electronics Symposium (IES), IEEE, 314-319.

Kingma, D. P. & Ba J.(2014) "Adam: A method for stochastic optimization," arXiv preprint arXiv:1412.6980.

Jiang, S. & Chen, Y. (2017)"Hand gesture recognition by using 3DCNN and LSTM with adam optimizer," in Pacific Rim Conference on Multimedia,  Springer, 743-753.

Paper, D.,(2020)"Scikit-Learn Classifier Tuning from Complex Training Sets," Hands-on Scikit-Learn for Machine Learning Applications: Data Science Fundamentals with Python, 165-188.

Brownlee, J. (2018) Better Deep Learning: Train Faster, Reduce Overfitting, and Make Better Predictions. Machine Learning Mastery.

Masters, D. & Luschi, C.(2018) "Revisiting small batch training for deep neural networks," arXiv preprint arXiv:1804.07612.