Ensemble fine tuned multi layer perceptron for predictive analysis of weather patterns and rainfall forecasting from satellite data (Record no. 22712)
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| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250424142651.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250424b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 25981 |
| Author | Surana, Amruta V. |
| 245 ## - TITLE STATEMENT | |
| Title | Ensemble fine tuned multi layer perceptron for predictive analysis of weather patterns and rainfall forecasting from satellite data |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.15(2), Oct |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Chennai |
| Name of publisher, distributor, etc. | ICT Academy |
| Year | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 3491-3496p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | The accurate prediction of weather patterns and rainfall forecasting is<br/>critical for various sectors, including agriculture, disaster<br/>management, and water resource planning. Traditional models often<br/>struggle to capture the complex interactions between atmospheric<br/>variables, particularly when integrating diverse types of satellite data<br/>(binary, categorical, and numerical). To address this challenge, an<br/>ensemble fine-tuned multi-layer perceptron (MLP) model is developed,<br/>combining the strengths of multiple machine learning techniques for<br/>more robust predictions. The primary problem is the difficulty in<br/>handling mixed data types while maintaining high prediction accuracy.<br/>Satellite data, including binary indicators (e.g., cloud presence),<br/>categorical features (e.g., cloud types), and numerical variables (e.g.,<br/>temperature, humidity, and wind speed), provide rich information but<br/>require specialized processing for effective forecasting. The proposed<br/>method involves fine-tuning an ensemble of MLP models with<br/>backpropagation, dropout regularization, and batch normalization to<br/>reduce overfitting and enhance generalization. The ensemble<br/>integrates predictions from individual MLP models, each trained on<br/>different subsets of features (binary, categorical, numerical). This<br/>technique allows the model to leverage complementary strengths and<br/>produce more accurate rainfall forecasts. Satellite data is preprocessed<br/>and normalized before training, and categorical variables are one-hot<br/>encoded to ensure compatibility with the MLP architecture. Results<br/>from testing on historical satellite weather datasets demonstrate<br/>significant improvements in forecast accuracy. The ensemble MLP<br/>achieved an accuracy of 91.3%, with a precision of 90.7%, recall of<br/>89.5%, and an F1-score of 90.1%. The model performed exceptionally<br/>well in identifying critical rainfall events, reducing false positives by<br/>12% compared to traditional models. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4622 |
| Topical term or geographic name entry element | Computer Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 25982 |
| Co-Author | Pawar, Suvarna Eknath |
| 773 0# - HOST ITEM ENTRY | |
| Title | ICTACT Journal on Soft Computing (IJSC) |
| Place, publisher, and date of publication | Chennai ICT Academy |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | https://ictactjournals.in/paper/IJSC_Vol_15_Iss_2_Paper_3_3491_3496.pdf |
| Link text | Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Articles Abstract Database |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | School of Engineering & Technology | School of Engineering & Technology | Archieval Section | 24/04/2025 | 2025-0661 | 24/04/2025 | 24/04/2025 | Articles Abstract Database |