Deep Learning for Interminable Rains Forecasting in Andhra Pradesh

Abstract

Rainfall is a key problem for practically every sort of individual in the community. It assists many sorts of elements in the country in various ways. It is the origin of drinkable liquid for certain communities. People, such as agricultural producers, rely on it for a living, as do all other humans. Agricultural workers will cultivate the land, and grains will be harvested for daily use. Rainfall, as a whole, serves as an important function for practically all types of individuals in community. Predictions of this downpour are usually intriguing and helpful data for all members of the community. It\'s a particularly helpful resource for governmental bodies since, depending on estimates; rainwater collection and storage may be planned far ahead. It also serves as an important part in the creation of electricity through the use of water movement in hydroelectric dams. In this paper, an effort is performed to implement a mechanism that analyzes prior year\'s precipitation information from a database obtained from the Andhra Pradesh (AP) Meteorological Agency and predicts the mean quantity of precipitation for the months to come in a given season. We established a classifier by separating the relevant information into learning and assessment information groups. We used several analytical, Deep Learning (DL) and Machine Learning (ML) methodologies, such as the Linear Regression Model (LRM), Support Vector Machine (SVM) algorithms, and a Neural Network (NN) model to forecast the findings, and we analyzed the findings and contrasted them to real world data. These diverse techniques reduced forecast inaccuracy and improved the reliability of the system\'s forecasted outcomes.

Introduction

Background

Agriculture is the primary source of livelihood and economic activity in India, especially in rural areas. Most farmers depend heavily on rainfall due to limited irrigation infrastructure and freshwater availability. Accurate precipitation forecasting is crucial not only for agriculture but also for drinking water availability and hydroelectric power generation.

Problem Statement

Rainfall prediction is a complex task influenced by various environmental factors. Traditional forecasting methods are often unreliable, leading to reduced crop yields, economic instability, and even farmer suicides. Hence, there's a growing need to adopt Machine Learning (ML) techniques for more accurate and actionable rainfall predictions.

Objectives

• Forecast monthly and annual precipitation in Andhra Pradesh (AP) using historical data from the Indian Meteorological Department (IMD).

• Develop long-term predictive models using ML techniques like LRM (Linear Regression Model), SVM (Support Vector Machine), and CNN (Convolutional Neural Networks).

• Minimize errors and improve prediction accuracy for agricultural planning and disaster preparedness.

Related Works

• Rainfall forecasting has been a key research area due to its critical role in agriculture, water management, and climate modeling.

• Several studies have used ML methods like ARIMA, ANN, SVM, and Self-Organizing Maps to model weather patterns.

• However, many prior studies focus on single-location datasets, lacking broader applicability and reliability.

Methodology

1. Data Collection & Preparation

   • Historical rainfall data (100+ years) from IMD.

   • Data normalization, error handling, and classification into real vs. expected rainfall.

   • Dataset split: 75% training, 25% testing.

2. ML Models Used

   • LRM (Linear Regression Model): Establishes a statistical relationship between key variables and rainfall output.

   • SVM (Support Vector Machine): Classifies and predicts rainfall amounts using a hyperplane-based approach.

   • CNN (Convolutional Neural Network): Processes large-scale data with minimal preprocessing, identifying complex rainfall patterns.

3. Evaluation

   • Models were trained and validated using rainfall data from random years (e.g., 1997, 2004).

   • Model performance was assessed via Mean Absolute Error (MAE).

Results & Discussion

• Rainfall trends indicated increased precipitation from August to October, with 1953 being the wettest year in the last century.

• The model performed well when predictions were compared with actual IMD data.

• MAE Comparison:

  • CNN: 52.76 (Best performance)

  • LRM: 68.80

  • SVM: 79.46

These results highlight the superior accuracy of CNNs in handling complex temporal and spatial rainfall data.

Conclusion

Machine learning offers a powerful and accurate approach to rainfall prediction in India. Among the methods tested, CNN showed the lowest error and highest reliability. Accurate forecasting is critical for:

• Agricultural planning

• Water resource management

• Mitigating climate-related risks

The study emphasizes the importance of leveraging historical data and advanced ML techniques to build robust predictive systems for rainfall in India’s agriculture-dependent economy.

Conclusion

In the present report, an initiative has been taken to construct a system for predicting precipitation in the Indian state of AP using DL and ML approaches. The findings of a simulation-based system using several methods were reported in the construction and obtain the information. Throughout the modeling of the techniques, certain locations in the network did not form groups; instead, those locations were separated from the groupings by a higher straight-line separation (Geometric Length). This issue was discovered in information from a prior month. As a consequence, the calculated number differed significantly from the real precipitation quantity for that period. In several situations, the simulated findings were more accurate than the real precipitation data. The present essay and effort may benefit common citizens as well as ranchers because livelihoods are mostly dependent on precipitation. Producers could learn about the probability of precipitation well before it occurs, reducing the amount of tragedies caused by severe precipitation and flooding in the long term.

References

[1] Waqas, Muhammad, et al. \"Potential of Artificial Intelligence-Based Techniques for Rainfall Forecasting in Thailand: A Comprehensive Review.\" Water 15.16 (2023): 2979. [2] N. Srinu, S. Sivahari and M. R. Kale, \"Leveraging Radial Basis Function Neural Networks for Rainfall Prediction in Andhra Pradesh,\" 2022 International Conference on Computer, Power and Communications (ICCPC), Chennai, India, 2022, pp. 474-477, doi: 10.1109/ICCPC55978.2022.10072177. [3] S. J. Basha, G. L. V. Prasad, K. Vivek, E. S. Kumar and T. Ammannamma, \"Leveraging Ensemble Time-series Forecasting Model to Predict the amount of Rainfall in Andhra Pradesh,\" 2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP), Vijayawada, India, 2022, pp. 1-7, doi: 10.1109/AISP53593.2022.9760553. [4] N. Srinu and B. H. Bindu, \"A Review on Machine Learning and Deep Learning based Rainfall Prediction Methods,\" 2022 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), Chennai, India, 2022, pp. 1-4, doi: 10.1109/ICPECTS56089.2022.10047554. [5] S. J. Basha, T. Ammannamma, K. Vivek and V. S. Veesam, \"Comparative Analysis of Time Series Forecasting Models to Predict Amount of Rainfall in Telangana,\" 2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2022, pp. 1918-1922, doi: 10.1109/ICACCS54159.2022.9785216. [6] P. P. G. Jaiswal et al., \"A Stacking Ensemble Learning Model for Rainfall Prediction based on Indian Climate,\" 2023 6th International Conference on Information Systems and Computer Networks (ISCON), Mathura, India, 2023, pp. 1-6, doi: 10.1109/ISCON57294.2023.10112077. [7] N. A, N. P. SS, R. R and K. G. R, \"Bi-LSTM Model based Real-time Rainfall Prediction,\" 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), Trichy, India, 2023, pp. 1078-1081, doi: 10.1109/ICAISS58487.2023.10250469. [8] C. Luo et al., \"A Practical Online Incremental Learning Framework for Precipitation Nowcasting,\" in IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-14, 2024, Art no. 5100114, doi: 10.1109/TGRS.2023.3330303. [9] S. Hu and X. Wang, \"Monthly Rainfall Prediction Based on VMD-GRA-Elman Model,\" 2023 4th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE), Nanjing, China, 2023, pp. 95-102, doi: 10.1109/ICBASE59196.2023.10303054. [10] B. Setya, R. A. Nurhidayatullah, M. B. Hewen and K. Kusrini, \"Comparative Analysis Of Rainfall Value Prediction In Semarang Using Linear And K-Nearest Neighbor Algorithms,\" 2023 5th International Conference on Cybernetics and Intelligent System (ICORIS), Pangkalpinang, Indonesia, 2023, pp. 1-5, doi: 10.1109/ICORIS60118.2023.10352274. [11] T. Aruna, P. Naresh, A. Rajeshwari, M. I. T. Hussan and K. G. Guptha, \"Visualization and Prediction of Rainfall Using Deep Learning and Machine Learning Techniques,\" 2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS), Tashkent, Uzbekistan, 2022, pp. 910-914, doi: 10.1109/ICTACS56270.2022.9988553. [12] Y. Wen, W. Yu, F. Zheng, D. Huang and N. Xiao, \"AdaNAS: Adaptively Post-processing with Self-supervised Neural Architecture Search for Ensemble Rainfall Forecasts,\" in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2024.3360636. [13] G. L. V. Prasad, B. R. Teja, T. Haribabu, G. N. Pavani, D. Karunamma and K. Vivek, \"A Hybrid Time Series Rainfall Prediction Model Using Neural Prophet and LS TM,\" 2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), Erode, India, 2023, pp. 1582-1587, doi: 10.1109/ICSSAS57918.2023.10331827. [14] P. Anuradha, V. Usha, K. Naga Lakshman, P. L. Tejaswi, T. Anusha and P. N. Sundari, \"Comparison of Time-Series Forecasting Models based on Prophets for Predicting Rainfall,\" 2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), Erode, India, 2023, pp. 1542-1545, doi: 10.1109/ICSSAS57918.2023.10331809. [15] G. L. Vara Prasad, B. Ravi Teja, S. Govathoti and S. Dhanikonda, \"Leveraging ARMA and ARMAX Time-Series Forecasting Models for Rainfall Prediction,\" 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2023, pp. 353-357, doi: 10.1109/ICACCS57279.2023.10113031. [16] P. Kiran, J. Vara, S. Kama, M. V. P. Kumar, D. Karunamma and K. Vivek, \"An Attention-based Rainfall Prediction System using LSTM Model and Convolutional Block Module,\" 2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), Erode, India, 2024, pp. 257-262, doi: 10.1109/ICSSAS64001.2024.10760287. [17] Y. Wang, B. Zhou, C. Zhang, S. W. Or, X. Gao and Z. Da, \"A Hybrid Data and Knowledge Driven Risk Prediction Method for Distributed Photovoltaic Systems Considering Spatio-Temporal Characteristics of Extreme Rainfalls,\" in IEEE Transactions on Industry Applications, vol. 61, no. 1, pp. 1613-1625, Jan.-Feb. 2025, doi: 10.1109/TIA.2024.3430247. [18] P. S. Sai, T. M. Thiyagu and P. G. Soumya, \"Rainfall Prediction using Machine Learning Algorithms,\" 2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), Salem, India, 2024, pp. 677-681, doi: 10.1109/ICAAIC60222.2024.10575126. [19] R. M. Adnan, A. Petroselli, S. Heddam, C. A. G. Santos and O. Kisi, \"Short term rainfall-runoff modelling using several machine learning methods and a conceptual event-based model\", Stochastic Environmental Research and Risk Assessment, vol. 35, no. 3, pp. 597-616, 2021. [20] F. R. Aderyani, S. J. Mousavi and F. Jafari, \"Short-term rainfall forecasting using machine learning-based approaches of PSOSVR LSTM and CNN\", Journal of Hydrology, vol. 614, pp. 128463, 2022.

Copyright

Copyright © 2025 V T Ram Pavan Kumar M, Anantha Vanitha, A V M Sai Pavan , Ch Naga Sai Sravanthi , D Venkata Karthik, K V Sai Lakshman. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.