A Review of Rainfall Prediction Using Machine Learning and Deep Learning
Keywords:
Artificial Intelligence, Deep learning, Indian Meteorological Dataset, Machine learning, Rainfall forecasting, Remote sensing, Satellite observationsAbstract
Exact rainfall forecasting is necessary in disaster management, long-term planning, agriculture, flood control, and water resource planning. In the past decade, there has been rapid development and enhancement in terms of data and computing technologies. The review presents a detailed description of new developments in satellite-based rainfall prediction, hydrological estimation, modelling, and especially changes from traditional methods. Current Artificial Intelligence and Machine Learning systems used in ground-based and satellite datasets consist of popular sources in India. GPM IMERG, Meteorological Department observation data, and CHIRPS datasets support large-scale modelling, but they are still challenged by issues of unequal spatial coverage, time, and variability in climatic regions. It is critical to deal with these problems to develop better prediction models. Machine Learning methods such as Random Forest, Gradient Boosting, Ensemble methods, and Support Vector Machines have been shown to perform well in short-term forecasting of rainfall, primarily because they work with sound input variables and detect hidden regularities. Deep learning models, including LSTM, CNN-based, and hybrid deep network models, also increase predictive ability by modelling detailed, non-linear, and spatiotemporal interdependencies that traditional models do not tend to reflect. This Artificial Intelligence-driven extreme forecasting is particularly beneficial for systems dealing with localised and monsoon rainfall variability.
References
A. Roccati, G. Paliaga, F. Luino, F. Faccini, and L. Turconi, “Rainfall Threshold for Shallow Landslides Initiation and Analysis of Long-Term Rainfall Trends in a Mediterranean Area,” Atmosphere, vol. 11, no. 12, p. 1367, Dec. 2020.
T. Gbangou, R. Sarku, E. Van Slobbe, F. Ludwig, G. Kranjac-Berisavljevic, and S. Paparrizos, “Coproducing Weather Forecast Information with and for Smallholder Farmers in Ghana: Evaluation and Design Principles,” Atmosphere, vol. 11, no. 9, p. 902, Aug. 2020.
V. Kumar, N. Kedam, K. V. Sharma, K. M. Khedher, and A. E. Alluqmani, “A Comparison of Machine Learning Models for Predicting Rainfall in Urban Metropolitan Cities,” Sustainability, vol. 15, no. 18, p. 13724, Sept. 2023.
N. Lohan, S. Kumar, V. Singh, R. P. Gupta, and G. Tiwari, “Analyzing an Extreme Rainfall Event in Himachal Pradesh, India, to Contribute to Sustainable Development,” Sustainability, vol. 17, no. 5, p. 2115, Feb. 2025.
N. Awasthi, J. N. Tripathi, G. P. Petropoulos, D. K. Gupta, A. K. Singh, and A. K. Kathwas, “Performance Assessment of Global-EO-Based Precipitation Products against Gridded Rainfall from the Indian Meteorological Department,” Remote Sensing, vol. 15, no. 13, p. 3443, July 2023.
W. A. Atiah, L. K. Amekudzi, J. N. A. Aryee, K. Preko, and S. K. Danuor, “Validation of Satellite and Merged Rainfall Data over Ghana, West Africa,” Atmosphere, vol. 11, no. 8, p. 859, Aug. 2020.
L. A. Espinosa, M. M. Portela, J. P. Matos, and S. Gharbia, “Climate Change Trends in a European Coastal Metropolitan Area: Rainfall, Temperature, and Extreme Events (1864–2021),” Atmosphere, vol. 13, no. 12, p. 1995, Nov. 2022.
S. H. Sanjeevaiah et al., “Understanding the Temporal Variability of Rainfall for Estimating Agro-Climatic Onset of Cropping Season over South Interior Karnataka, India,” Agronomy, vol. 11, no. 6, p. 1135, June 2021.
V. Nitha, S. K. Pramada, N. S. Praseed, and V. Sridhar, “Performance Evaluation of Numerical Weather Prediction Models in Forecasting Rainfall Events in Kerala, India,” Atmosphere, vol. 16, no. 4, p. 372, Mar. 2025.
U. Nikhil, A. Pandiyan, S. Raja, and Z. Stamenkovic, “Machine Learning-Based Crop Yield Prediction in South India: Performance Analysis of Various Models,” Computers, vol. 13, no. 6, p. 137, May 2024.
P. O. Bojang, T.-C. Yang, Q. B. Pham, and P.-S. Yu, “Linking Singular Spectrum Analysis and Machine Learning for Monthly Rainfall Forecasting,” Applied Sciences, vol. 10, no. 9, p. 3224, May 2020.
V. Kolluru, S. Kolluru, N. Wagle, and T. D. Acharya, “Secondary Precipitation Estimate Merging Using Machine Learning: Development and Evaluation over Krishna River Basin, India,” Remote Sensing, vol. 12, no. 18, p. 3013, Sept. 2020.
A. Jayasree, S. K. Sasidharan, R. Sivadas, and J. A. Ramakrishnan, “Hybrid EMD-RF Model for Predicting Annual Rainfall in Kerala, India,” Applied Sciences, vol. 13, no. 7, p. 4572, Apr. 2023.
S. He, J. Wang, and S. Liu, “Rainfall Event–Duration Thresholds for Landslide Occurrences in China,” Water, vol. 12, no. 2, p. 494, Feb. 2020.
K. Skarlatos, E. S. Bekri, D. Georgakellos, P. Economou, and S. Bersimis, “Projecting Annual Rainfall Timeseries Using Machine Learning Techniques,” Energies, vol. 16, no. 3, p. 1459, Feb. 2023.
W. Bai et al., “Applications of GNSS-RO to Numerical Weather Prediction and Tropical Cyclone Forecast,” Atmosphere, vol. 11, no. 11, p. 1204, Nov. 2020.
A. K. Singh et al., “An Integrated Statistical-Machine Learning Approach for Runoff Prediction,” Sustainability, vol. 14, no. 13, p. 8209, July 2022.
K.-S. Choo, D.-H. Kang, and B.-S. Kim, “Impact Assessment of Urban Flood on Traffic Disruption using Rainfall–Depth–Vehicle Speed Relationship,” Water, vol. 12, no. 4, p. 926, Mar. 2020.
K. Yadav, S. Malviya, and A. K. Tiwari, “Improving Weather Forecasting in Remote Regions Through Machine Learning,” Atmosphere, vol. 16, no. 5, p. 587, May 2025.
G. Ch. Satyanarayana, V. Sambasivarao, P. Yasaswini, and M. M. Ali, “Estimating Daily Temperatures over Andhra Pradesh, India, Using Artificial Neural Networks,” Atmosphere, vol. 14, no. 10, p. 1501, Sept. 2023.
J. Estévez, J. A. Bellido-Jiménez, X. Liu, and A. P. García-Marín, “Monthly Precipitation Forecasts Using Wavelet Neural Networks Models in a Semiarid Environment,” Water, vol. 12, no. 7, p. 1909, July 2020.
S. K. Patakamuri, K. Muthiah, and V. Sridhar, “Long-Term Homogeneity, Trend, and Change-Point Analysis of Rainfall in the Arid District of Ananthapuramu, Andhra Pradesh State, India,” Water, vol. 12, no. 1, p. 211, Jan. 2020.
K. Pandey, S. Kumar, A. Malik, and A. Kuriqi, “Artificial Neural Network Optimized with a Genetic Algorithm for Seasonal Groundwater Table Depth Prediction in Uttar Pradesh, India,” Sustainability, vol. 12, no. 21, p. 8932, Oct. 2020.
P. Punde, N. Nischal, R. Attada, D. Aggarwal, and C. Radhakrishnan, “Numerical Simulation of Winter Precipitation over the Western Himalayas Using a Weather Research and Forecasting Model during 2001–2016,” Climate, vol. 10, no. 11, p. 160, Oct. 2022.
M. A. Samad and D.-Y. Choi, “Learning-Assisted Rain Attenuation Prediction Models,” Applied Sciences, vol. 10, no. 17, p. 6017, Aug. 2020.
J. H. Lee, G. M. Yuk, H. T. Moon, and Y.-I. Moon, “Integrated Flood Forecasting and Warning System against Flash Rainfall in the Small-Scaled Urban Stream,” Atmosphere, vol. 11, no. 9, p. 971, Sept. 2020.
X. Wu et al., “The Development of a Hybrid Wavelet-ARIMA-LSTM Model for Precipitation Amounts and Drought Analysis,” Atmosphere, vol. 12, no. 1, p. 74, Jan. 2021.
M. M. Portela, L. A. Espinosa, and M. Zelenakova, “Long-Term Rainfall Trends and Their Variability in Mainland Portugal in the Last 106 Years,” Climate, vol. 8, no. 12, p. 146, Dec. 2020.
T. S. Alsumaiti, K. Hussein, D. T. Ghebreyesus, and H. O. Sharif, “Performance of the CMORPH and GPM IMERG Products over the United Arab Emirates,” Remote Sensing, vol. 12, no. 9, p. 1426.
K. D. Dinh, T. N. Anh, N. Y. Nguyen, Du D. Bui, and R. Srinivasan, “Evaluation of Grid-Based Rainfall Products and Water Balances over the Mekong River Basin,” Remote Sensing, vol. 12, no. 11, p. 1858, June 2020.
M. Łoś, K. Smolak, G. Guerova, and W. Rohm, “GNSS-Based Machine Learning Storm Nowcasting,” Remote Sensing, vol. 12, no. 16, p. 2536, Aug. 2020.
M. T. Mahmoud, S. A. Mohammed, M. A. Hamouda, and M. M. Mohamed, “Impact of Topography and Rainfall Intensity on the Accuracy of IMERG Precipitation Estimates in an Arid Region,” Remote Sensing, vol. 13, no. 1, p. 13, Dec. 2020.
L. Li et al., “An Improved Method for Rainfall Forecast Based on GNSS-PWV,” Remote Sensing, vol. 14, no. 17, p. 4280, Aug. 2022.
