JACIII Vol.28 No.1 pp. 5-11
doi: 10.20965/jaciii.2024.p0005

Research Paper:

rain-t: Daily Rainfall Predictive Model Using 6-Gene Genetic Expression for Historical Data-Based Forecasting

Marvin Jade Genoguin*1,*2,† ORCID Icon, Ronnie S. Concepcion II*1,*3 ORCID Icon, Andres Philip Mayol*1,*3 ORCID Icon, Aristotle Ubando*1,*4 ORCID Icon, Alvin Culaba*1,*4 ORCID Icon, and Elmer P. Dadios*1,*3 ORCID Icon

*1Center for Engineering and Sustainability Development Research, De La Salle University
2401 Taft Avenue, Malate, Manila 1004, Philippines

*2Department of Civil Engineering, Eastern Visayas State University
Lino Gonzaga Avenue, Tacloban City 6500, Philippines

*3Department of Manufacturing Engineering and Management, De La Salle University
2401 Taft Avenue, Malate, Manila 1004, Philippines

*4Department of Mechanical Engineering, De La Salle University
2401 Taft Avenue, Malate, Manila 1004, Philippines

Corresponding author

April 22, 2022
July 10, 2022
January 20, 2024
computational intelligence, data orchestration and transformation, multigene genetic programming, rainfall forecasting

Extreme weather conditions such as heavy rainfalls have been wreaking havoc not only in urban areas but also in an entire watershed. The development of a flood management plan and flood mitigating structures to alleviate the impacts of flooding is very crucial because it needs intensive and continuous historical data. However, missing data due to equipment failure that gathers the rainfall data could be a problem. Rainfall data is not only useful in designing flood mitigating structures but also in planning our day-to-day activities ahead of time. To address this problem, this paper proposes a predictive model which able to forecast in a short lead-time and predict missing data within the dataset. In this paper, three predictive models will be compared namely recurrent neural network, Gaussian processing regression, and the proposed 6-gene genetic expression-based predictive modeling (MGGP). 29-year 24-hour cumulative rainfall data which were sourced in PAGASA Tacloban city weather station, Philippines, was used. The data were cleaned by removing negative values. Two datasets were created, the first (RFDS1) dataset which makes use of three indices (year, month, and days), and the second (RFDS2) dataset which was orchestrated and transformed to increase correlation and reduce prediction errors which had an additional two datasets (ave(t-1,t-2),t-1). Each method used three and five time-based indices. The result shows an erratic behavior of the model from three methods that used the RFDS1, while RFDS2 had a more stable predictive model. This shows that the data orchestration and transformation greatly improved the correlation and reduced errors. However, MGGP showed the best results among the three methods.

rain-t model development architecture

rain-t model development architecture

Cite this article as:
M. Genoguin, R. Concepcion II, A. Mayol, A. Ubando, A. Culaba, and E. Dadios, “rain-t: Daily Rainfall Predictive Model Using 6-Gene Genetic Expression for Historical Data-Based Forecasting,” J. Adv. Comput. Intell. Intell. Inform., Vol.28 No.1, pp. 5-11, 2024.
Data files:
  1. [1] R. Concepcion, E. Dadios, J. Cuello, A. Bandala, E. Sybingco, and R. R. Vicerra, “Determination of aquaponic water macronutrient concentrations based on lactuca sativa leaf photosynthetic signatures using hybrid gravitational search and recurrent neural network,” Walailak J. of Science and Technology, Vol.18, No.10, 2021.
  2. [2] R. Concepcion, E. Dadios, J. Alejandrino, C. H. Mendigoria, H. Aquino, and O. J. Alajas, “Diseased surface assessment of maize cercospora leaf spot using hybrid gaussian quantum-behaved particle swarm and recurrent neural network,” Proc. IEEE Int. IOT, Electronics and Mechatronics Conf. (IEMTRONICS), 2021.
  3. [3] O. J. Alajas et al., “Indirect Prediction of Aquaponic Water Nitrate Concentration Using Hybrid Genetic Algorithm and Recurrent Neural Network,” 2021 IEEE 13th Int. Conf. on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), 2021.
  4. [4] IPCC, “Assessment Report 6 Climate Change 2021: The Physical Science Basis,” 2021.
  5. [5] A. D. Mehr, “Seasonal rainfall hindcasting using ensemble multi-stage genetic programming,” Theoretical and Applied Climatology, Vol.143, Nos.1-2, pp. 461-472, 2021.
  6. [6] S. Cramer, M. Kampouridis, and A. A. Freitas, “Decomposition genetic programming: An extensive evaluation on rainfall prediction in the context of weather derivatives,” Applied Soft Computing J., Vol.70, pp. 208-224, 2018.
  7. [7] R. Srinivas, A. P. Singh, and A. Deshmukh, “Development of a HEC-HMS-based watershed modeling system for identification, allocation, and optimization of reservoirs in a river basin,” Environmental Monitoring and Assessment, Vol.190, No.1, 2018.
  8. [8] D. Abdulbaki, M. Al-Hindi, A. Yassine, and M. Abou Najm, “An optimization model for the allocation of water resources,” J. of Cleaner Production, Vol.164, pp. 994-1006, 2017.
  9. [9] M. Das and S. K. Ghosh, “Data-driven approaches for meteorological time series prediction: A comparative study of the state-of-the-art computational intelligence techniques,” Pattern Recognition Letters, Vol.105, pp. 155-164, 2018.
  10. [10] T. Partal, H. K. Cigizoglu, and E. Kahya, “Daily precipitation predictions using three different wavelet neural network algorithms by meteorological data,” Stochastic Environmental Research and Risk Assessment, Vol.29, No.5, pp. 1317-1329, 2015.
  11. [11] U. Thissen, R. Van Brakel, A. P. De Weijer, W. J. Melssen, and L. M. C. Buydens, “Using support vector machines for time series prediction,” Chemometrics and Intelligent Laboratory Systems, Vol.69, Nos.1-2, pp. 35-49, 2003.
  12. [12] A. D. Mehr, “An ensemble genetic programming model for seasonal precipitation forecasting,” SN Applied Sciences, Vol.2, No.11, pp. 1-14, 2020.
  13. [13] R. Concepcion, S. Lauguico, E. Dadios, A. Bandala, E. Sybingco, and J. Alejandrino, “Tomato septoria leaf spot necrotic and chlorotic regions computational assessment using artificial bee colony-optimized leaf disease index,” 2020 IEEE Region 10 Annual Int. Conf. (TENCON), pp. 1243-1248, 2020.
  14. [14] M. F. Bekçioğulları et al., “Comparison of different machine learning methods in short-term forecasting of solar energy,” EMO Bilimsel Dergi, Vol.11, No.22, pp. 37-45, 2021 (in Turkish).
  15. [15] R. Concepcion, S. Lauguico, J. Alejandrino, J. de Guia, E. P. Dadios, and A. Bandala, “Aquaphotomics Determination of Total Organic Carbon and Hydrogen Biomarkers on Aquaponic Pond Water and Concentration Prediction Using Genetic Programming,” 2020 IEEE 8th Region 10 Humanitarian Technology Conf. (R10-HTC), 2020.
  16. [16] R. Concepcion, E. P. Dadios, and J. Cuello, “Non-destructive in situ measurement of aquaponic lettuce leaf photosynthetic pigments and nutrient concentration using hybrid genetic programming,” Agrivita, Vol.43, No.3, 2021.
  17. [17] M. G. Palconit et al., “FishEye: A Centroid-Based Stereo Vision Fish Tracking Using Multigene Genetic Programming,” 2021 IEEE 9th Region 10 Humanitarian Technology Conf. (R10-HTC), 2021.
  18. [18] N. Mishra, M. Tech Scholar, and A. Kushwaha, “Rainfall Prediction using Gaussian Process Regression Classifier,” Int. J. of Advanced Research in Computer Engineering & Technology (IJARCET), Vol.8, No.8, pp. 392-397, 2019.
  19. [19] A. D. Mehr, M. Jabarnejad, and V. Nourani, “Pareto-optimal MPSA-MGGP: A new gene-annealing model for monthly rainfall forecasting,” J. of Hydrology, Vol.571, pp. 406-415, 2019.

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Last updated on Feb. 19, 2024