Accurate precipitation forecasting is crucial for mitigating the impacts of extreme weather events and enhancing disaster preparedness. This study evaluates the performance of Long Short-Term Memory and Bidirectional LSTM models in predicting hourly precipitation in Dar es Salaam using a multivariate time-series approach. The dataset consists of temperature, pressure, U-wind, V-wind, and precipitation, preprocessed to handle missing values and normalized to improve model performance. Performance metrics indicate that BiLSTM outperforms LSTM, achieving lower Mean Absolute Error and Root Mean Squared Error by 6.4% and 6.5%, respectively along with improved threshold scores. It demonstrated better overall prediction accuracy. It also improves moderate precipitation detection (TS3.0) by 16.9% compared to LSTM. These results highlight the advantage of bidirectional processing in capturing complex atmospheric patterns, making BiLSTM a more effective approach for precipitation forecasting. The findings contribute to the development of improved deep learning models for early warning systems and climate risk management.
References
[1]
Ali, M., Deo, R. C., Xiang, Y., Li, Y., & Yaseen, Z. M. (2020). Forecasting Long-Term Precipitation for Water Resource Management: A New Multi-Step Data-Intelligent Modelling Approach. Hydrological Sciences Journal, 65, 2693-2708. https://doi.org/10.1080/02626667.2020.1808219
[2]
Barros, A. P., & Lettenmaier, D. P. (1994). Dynamic Modeling of Orographically Induced Precipitation. Reviews of Geophysics, 32, 265-284. https://doi.org/10.1029/94rg00625
[3]
Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning Long-Term Dependencies with Gradient Descent Is Difficult. IEEE Transactions on Neural Networks, 5, 157-166. https://doi.org/10.1109/72.279181
[4]
Che, Z., Purushotham, S., Cho, K., Sontag, D., & Liu, Y. (2018). Recurrent Neural Networks for Multivariate Time Series with Missing Values. Scientific Reports, 8, Article No. 6085. https://doi.org/10.1038/s41598-018-24271-9
[5]
Ebtehaj, I., & Bonakdari, H. (2024). CNN Vs. LSTM: A Comparative Study of Hourly Precipitation Intensity Prediction as a Key Factor in Flood Forecasting Frameworks. Atmosphere, 15, 1082. https://doi.org/10.3390/atmos15091082
[6]
Elman, J. L. (1990). Finding Structure in Time. Cognitive Science, 14, 179-211. https://doi.org/10.1207/s15516709cog1402_1
[7]
Gers, F. A., Schmidhuber, J., & Cummins, F. (2000). Learning to Forget: Continual Prediction with Lstm. Neural Computation, 12, 2451-2471. https://doi.org/10.1162/089976600300015015
[8]
Graves, A. (2012). Supervised Sequence Labelling. In Studies in Computational Intelligence (pp. 5-13). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-24797-2_2
[9]
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J. et al. (2020). The ERA5 Global Reanalysis. Quarterly Journal of the Royal Meteorological Society, 146, 1999-2049. https://doi.org/10.1002/qj.3803
[10]
Hess, P., & Boers, N. (2022). Deep Learning for Improving Numerical Weather Prediction of Heavy Rainfall. Journal of Advances in Modeling Earth Systems, 14, e2021MS002765. https://doi.org/10.1029/2021ms002765
[11]
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9, 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
[12]
Hwang, J., Lee, S., Gil, J., & Lee, C. (2024). Determination of Optimal Batch Size of Deep Learning Models with Time Series Data. Sustainability, 16, Article 5936. https://doi.org/10.3390/su16145936
[13]
Jerome Glago, F. (2021). Flood Disaster Hazards; Causes, Impacts and Management: A State-of-the-Art Review. In Natural Hazards—Impacts, Adjustments and Resilience. Intech Open. https://doi.org/10.5772/intechopen.95048
[14]
Kai, K. H., Ngwali, M. K., & Faki, M. M. (2021). Assessment of the Impacts of Tropical Cyclone Fantala to Tanzania Coastal Line: Case Study of Zanzibar. Atmospheric and Climate Sciences, 11, 245-266. https://doi.org/10.4236/acs.2021.112015
[15]
Kim, Y., Kim, M. K., Fu, N., Liu, J., Wang, J., & Srebric, J. (2025). Investigating the Impact of Data Normalization Methods on Predicting Electricity Consumption in a Building Using Different Artificial Neural Network Models. Sustainable Cities and Society, 118, Article 105570. https://doi.org/10.1016/j.scs.2024.105570
[16]
Kratzert, F., Klotz, D., Brenner, C., Schulz, K., & Herrnegger, M. (2018). Rainfall-Runoff Modelling Using Long Short-Term Memory (LSTM) Networks. Hydrology and Earth System Sciences, 22, 6005-6022. https://doi.org/10.5194/hess-22-6005-2018
[17]
Lipton, Z. C. (2015). A Critical Review of Recurrent Neural Networks for Sequence Learning. http://export.arxiv.org/pdf/1506.00019
[18]
Mason, S. (2016). Guidance on Verification of Operational Seasonal Climate Forecasts. https://doi.org/10.7916/d8-gh4a-ex60
[19]
Moeletsi, M. E., Mellaart, E. A. R., Mpandeli, N. S., & Hamandawana, H. (2013). The Use of Rainfall Forecasts as a Decision Guide for Small-Scale Farming in Limpopo Province, South Africa. The Journal of Agricultural Education and Extension, 19, 133-145. https://doi.org/10.1080/1389224x.2012.734253
[20]
Nambirajan, V., & Rajalakshmi, V. (2024). Climatological Rainfall Forecasting Using LSTM: An Analysis of Sequential Input and Data Window Input Approaches. In Lecture Notes in Networks and Systems (pp. 311-321). Springer. https://doi.org/10.1007/978-981-99-7814-4_25
[21]
NBS (2022). Tanzania Population and Housing Census 2022. National Demographic Socio-Economic Profile. https://microdata.nbs.go.tz/index.php/catalog/45
[22]
Owiti, Z. (2012). Spatial Distribution of Rainfall Seasonality over East Africa. Journal of Geography and Regional Planning, 5, 409-421. https://doi.org/10.5897/jgrp12.027
[23]
Piran, M. J., Wang, X., Kim, H. J., & Kwon, H. H. (2024). Precipitation Nowcasting Using Transformer-Based Generative Models and Transfer Learning for Improved Disaster Preparedness. International Journal of Applied Earth Observation and Geoinformation, 132, Article 103962. https://doi.org/10.1016/j.jag.2024.103962
[24]
Priatna, M. A., & Djamal, E. C. (2020). Precipitation Prediction Using Recurrent Neural Networks and Long Short-Term Memory. TELKOMNIKA (Telecommunication Computing Electronics and Control), 18, 2525. https://doi.org/10.12928/telkomnika.v18i5.14887
[25]
Sakijege, T., Lupala, J., & Sheuya, S. (2012). Flooding, Flood Risks and Coping Strategies in Urban Informal Residential Areas: The Case of Keko Machungwa, Dar Es Salaam, Tanzania. Jàmbá: Journal of Disaster Risk Studies, 4, a46. https://doi.org/10.4102/jamba.v4i1.46
[26]
Schuster, M., & Paliwal, K. K. (1997). Bidirectional Recurrent Neural Networks. IEEE Transactions on Signal Processing, 45, 2673-2681. https://doi.org/10.1109/78.650093
[27]
Sherstinsky, A. (2020). Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network. Physica D: Nonlinear Phenomena, 404, Article 132306. https://doi.org/10.1016/j.physd.2019.132306
[28]
Siami-Namini, S., Tavakoli, N., & Namin, A. S. (2019). A Comparative Analysis of Forecasting Financial Time Series Using ARIMA, LSTM, and BiLSTM. https://arxiv.org/abs/1911.09512v1
[29]
Waqas, M., & Humphries, U. W. (2024). A Critical Review of RNN and LSTM Variants in Hydrological Time Series Predictions. MethodsX, 13, 102946. https://doi.org/10.1016/j.mex.2024.102946
[30]
Wu, Y. T., & Xue, W. (2024). Data-Driven Weather Forecasting and Climate Modeling from the Perspective of Development. Atmosphere, 15, Article 689. https://doi.org/10.3390/atmos15060689
[31]
Xu, Y., Hu, C., Wu, Q., Jian, S., Li, Z., Chen, Y. et al. (2022). Research on Particle Swarm Optimization in LSTM Neural Networks for Rainfall-Runoff Simulation. Journal of Hydrology, 608, Article 127553. https://doi.org/10.1016/j.jhydrol.2022.127553
[32]
Zhang, X., Shi, J., Chen, H., Xiao, Y., & Zhang, M. (2023). Precipitation Prediction Based on CEEMDAN-VMD-BILSTM Combined Quadratic Decomposition Model. Water Supply, 23, 3597-3613. https://doi.org/10.2166/ws.2023.212
