Real-Time Rain Prediction in Agriculture using AI and IoT: A Bi-Directional LSTM Approach

Authors

  • Radhika Peeriga Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India | Department of Artificial Intelligence and Data Science, KL University, Vijayawada, India
  • Dhruva R. Rinku Department of ECE, CVR College of Engineering, Hyderabad, Telangana, India
  • J. Uday Bhaskar Department of EEE, Malla Reddy Engineering College, Hyderabad, Telangana, India
  • Rajeswaran Nagalingam Department of EEE, Malla Reddy College of Engineering, Secunderabad, Telangana, India
  • Fahd M. Aldosari Computer and Network Engineering Department, College of Computing, Umm Al-Qura University, Makkah, 21955, Saudi Arabia
  • Hussain M. Albarakati Computer and Network Engineering Department, College of Computing, Umm Al-Qura University, Makkah, 21955, Saudi Arabia
  • Ayman A. Alharbi Computer and Network Engineering Department, College of Computing, Umm Al-Qura University, Makkah, 21955, Saudi Arabia
  • Amar Y. Jaffar Computer and Network Engineering Department, College of Computing, Umm Al-Qura University, Makkah, 21955, Saudi Arabia
Volume: 14 | Issue: 4 | Pages: 15805-15812 | August 2024 | https://doi.org/10.48084/etasr.8011

Abstract

Accurate rain forecasting is crucial for optimizing agricultural practices and improving crop yields. This study presents a real-time rain forecasting model using a Bidirectional Long Short-Term Memory (Bi-LSTM) algorithm for an on-device AI platform. The model uses historical weather data to predict rainfall, enabling farmers to make data-driven decisions in irrigation, pest control, and field operations. This model enables farmers to optimize water use, conserve energy, and improve overall resource management. Real-time capabilities allow immediate adjustments to agricultural activities, mitigating risks associated with unexpected weather changes. The Bi-LSTM model achieved a mean accuracy of 92%, significantly outperforming the traditional LSTM (85%) and ARIMA (80%) models. This high accuracy is attributed to the model's bidirectional processing capability, which captures comprehensive temporal patterns in the weather data. Implementing this model can enhance decision-making processes for farmers, resulting in increased productivity and profitability in the agricultural sector.

Keywords:

AI, IoT, Bi-LSTM, rain prediction, precision farming, smart agriculture, weather forecasting

Downloads

Download data is not yet available.

References

M. Usman Saeed Khan, K. Mohammad Saifullah, A. Hussain, and H. Mohammad Azamathulla, "Comparative analysis of different rainfall prediction models: A case study of Aligarh City, India," Results in Engineering, vol. 22, Jun. 2024, Art. no. 102093.

S. Poornima and M. Pushpalatha, "Prediction of Rainfall Using Intensified LSTM Based Recurrent Neural Network with Weighted Linear Units," Atmosphere, vol. 10, no. 11, Nov. 2019, Art. no. 668.

N. C. Eli-Chukwu, "Applications of Artificial Intelligence in Agriculture: A Review," Engineering, Technology & Applied Science Research, vol. 9, no. 4, pp. 4377–4383, Aug. 2019.

S. J. Oad, H. Maqsood, A. L. Qureshi, S. Ahmed, I. A. Channa, and M. I. Ali, "Farm-based Evaluation of Sustainable Alternative Irrigation Practices," Engineering, Technology & Applied Science Research, vol. 9, no. 3, pp. 4310–4314, Jun. 2019.

P. Suebsombut, A. Sekhari, P. Sureephong, A. Belhi, and A. Bouras, "Field Data Forecasting Using LSTM and Bi-LSTM Approaches," Applied Sciences, vol. 11, no. 24, Jan. 2021, Art. no. 11820.

D. Kaplun et al., "An intelligent agriculture management system for rainfall prediction and fruit health monitoring," Scientific Reports, vol. 14, no. 1, Jan. 2024, Art. no. 512.

A. G. Salman, Y. Heryadi, E. Abdurahman, and W. Suparta, "Single Layer & Multi-layer Long Short-Term Memory (LSTM) Model with Intermediate Variables for Weather Forecasting," Procedia Computer Science, vol. 135, pp. 89–98, Jan. 2018.

K. Fang, D. Kifer, K. Lawson, and C. Shen, "Evaluating the Potential and Challenges of an Uncertainty Quantification Method for Long Short-Term Memory Models for Soil Moisture Predictions," Water Resources Research, vol. 56, no. 12, 2020, Art. no. e2020WR028095.

D. Feng, K. Fang, and C. Shen, "Enhancing Streamflow Forecast and Extracting Insights Using Long-Short Term Memory Networks With Data Integration at Continental Scales," Water Resources Research, vol. 56, no. 9, 2020, Art. no. e2019WR026793.

G. Thottungal Harilal, A. Dixit, and G. Quattrone, "Establishing hybrid deep learning models for regional daily rainfall time series forecasting in the United Kingdom," Engineering Applications of Artificial Intelligence, vol. 133, Jul. 2024, Art. no. 108581.

I. Salehin, I. M. Talha, Md. Mehedi Hasan, S. T. Dip, Mohd. Saifuzzaman, and N. N. Moon, "An Artificial Intelligence Based Rainfall Prediction Using LSTM and Neural Network," in 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), Bhubaneswar, India, Dec. 2020, pp. 5–8.

A. Y. Barrera-Animas, L. O. Oyedele, M. Bilal, T. D. Akinosho, J. M. D. Delgado, and L. A. Akanbi, "Rainfall prediction: A comparative analysis of modern machine learning algorithms for time-series forecasting," Machine Learning with Applications, vol. 7, Mar. 2022, Art. no. 100204.

A. Rahman et al., "Rainfall Prediction System Using Machine Learning Fusion for Smart Cities," Sensors, vol. 22, no. 9, Jan. 2022, Art. no. 3504.

D. H. Nguyen, J. B. Kim, and D. H. Bae, "Improving Radar-Based Rainfall Forecasts by Long Short-Term Memory Network in Urban Basins," Water, vol. 13, no. 6, Jan. 2021, Art. no. 776.

S. Narejo, M. M. Jawaid, S. Talpur, R. Baloch, and E. G. A. Pasero, "Multi-step rainfall forecasting using deep learning approach," PeerJ Computer Science, vol. 7, May 2021, Art. no. e514.

S. Ravuri et al., "Skilful precipitation nowcasting using deep generative models of radar," Nature, vol. 597, no. 7878, pp. 672–677, Sep. 2021.

S. Indhumathi, S. Aghalya, S. J. A, and P. Aarthi M, "IoT-Enabled Weather Monitoring and Rainfall Prediction using Machine Learning Algorithm," in 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), Trichy, India, Aug. 2023, pp. 1491–1495.

N. Pierre et al., "AI Based Real-Time Weather Condition Prediction with Optimized Agricultural Resources," European Journal of Technology, vol. 7, no. 2, pp. 36–49, Jun. 2023.

L. Ye, S. F. Jabbar, M. M. Abdul Zahra, and M. L. Tan, "Bayesian Regularized Neural Network Model Development for Predicting Daily Rainfall from Sea Level Pressure Data: Investigation on Solving Complex Hydrology Problem," Complexity, vol. 2021, no. 1, 2021, Art. no. 6631564.

C. C. Wei and C. C. Hsu, "Real-Time Rainfall Forecasts Based on Radar Reflectivity during Typhoons: Case Study in Southeastern Taiwan," Sensors, vol. 21, no. 4, Jan. 2021, Art. no. 1421.

S. Sarkar, A. Srivastava, and Er. A. Kaur, "Prediction Rainfall with Regression Analysis," International Journal for Research in Applied Science and Engineering Technology, vol. 11, no. 3, pp. 1934–1943, Mar. 2023.

Z. Alizadeh, J. Yazdi, and M. S. Najafi, "Improving the outputs of regional heavy rainfall forecasting models using an adaptive real-time approach," Hydrological Sciences Journal, vol. 67, no. 4, pp. 550–563, Mar. 2022.

M. S. Muneer, S. M. Nabeel Mustafa, S. S. Zehra, and H. Maqsood, "Rain Predictive Model using Machine learning Techniques," in 2023 International Multi-disciplinary Conference in Emerging Research Trends (IMCERT), Karachi, Pakistan, Jan. 2023, vol. I, pp. 1–6.

D. V. Rayudu and J. F. Roseline, "Accurate Weather Forecasting for Rainfall Prediction Using Artificial Neural Network Compared with Deep Learning Neural Network," in 2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF), Chennai, India, Jan. 2023, pp. 1–6.

"Weather dataset." [Online]. Available: https://www.kaggle.com/datasets/balams81/weather-dataset.

D. H. Nguyen, J.-B. Kim, and D.-H. Bae, "Improving Radar-Based Rainfall Forecasts by Long Short-Term Memory Network in Urban Basins," Water, vol. 13, no. 6, Jan. 2021, Art. no. 776.

A. Rahman et al., "Rainfall Prediction System Using Machine Learning Fusion for Smart Cities," Sensors, vol. 22, no. 9, Jan. 2022, Art. no. 3504.

Downloads

How to Cite

[1]
Peeriga, R., Rinku, D.R., Bhaskar, J.U., Nagalingam, R., Aldosari, F.M., Albarakati, H.M., Alharbi, A.A. and Jaffar, A.Y. 2024. Real-Time Rain Prediction in Agriculture using AI and IoT: A Bi-Directional LSTM Approach. Engineering, Technology & Applied Science Research. 14, 4 (Aug. 2024), 15805–15812. DOI:https://doi.org/10.48084/etasr.8011.

Metrics

Abstract Views: 432
PDF Downloads: 387

Metrics Information

Most read articles by the same author(s)