Cite this DOI

Longman Publishers (2020). Rainfall Forecasting Based on Surface Data of Chennai Region Using Artificial Neural Networks. *Journal of Science & Technology*, 26–36. https://doi.org/10.46243/jst.2020.v5.i6.pp26-36

@article{anon2020rainfall,
  title     = {{Rainfall Forecasting Based on Surface Data of Chennai Region Using Artificial Neural Networks}},
  journal   = {Journal of Science \& Technology},
  year      = {2020},
  month     = {sep},
  number    = {Volume 5},
  pages     = {26--36},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2020.v5.i6.pp26-36},
  url       = {https://doi.org/10.46243/jst.2020.v5.i6.pp26-36},
  language  = {en},
  abstract  = {In this study, we developed user friendly rainfall forecasting system based on Back propagation Neural Network using MATLAB 7.10 to forecast Hourly rainfall in Chennai region. The dataset of 31488 samples has been collected from Nungambakkam Meteorological Station, Chennai for the period of 2005 to 2015. The data was organized into day-wise hourly recordings as well as day-wise, maximum, minimum, average data of Relative Humidity (RH), Temperature, Pressure and Wind Speed along with Rainfall data. The collected dataset has been used both for training and for testing the data. The developed system gives more accuracy of 94.8197\% when the training data set is 55\% and the testing data set is 45\% with least Mean Squared Error (MSE) value 0.012437.}
}

TY  - JOUR
TI  - Rainfall Forecasting Based on Surface Data of Chennai Region Using Artificial Neural Networks
JO  - Journal of Science & Technology
PY  - 2020
DA  - 2020/09/24/
IS  - Volume 5
SP  - 26
EP  - 36
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - In this study, we developed user friendly rainfall forecasting system based on Back propagation Neural Network using MATLAB 7.10 to forecast Hourly rainfall in Chennai region. The dataset of 31488 samples has been collected from Nungambakkam Meteorological Station, Chennai for the period of 2005 to 2015. The data was organized into day-wise hourly recordings as well as day-wise, maximum, minimum, average data of Relative Humidity (RH), Temperature, Pressure and Wind Speed along with Rainfall data. The collected dataset has been used both for training and for testing the data. The developed system gives more accuracy of 94.8197% when the training data set is 55% and the testing data set is 45% with least Mean Squared Error (MSE) value 0.012437.
DO  - 10.46243/jst.2020.v5.i6.pp26-36
UR  - https://doi.org/10.46243/jst.2020.v5.i6.pp26-36
ER  -

{
    "type": "article-journal",
    "id": "10.46243/jst.2020.v5.i6.pp26-36",
    "DOI": "10.46243/jst.2020.v5.i6.pp26-36",
    "URL": "https://doi.org/10.46243/jst.2020.v5.i6.pp26-36",
    "title": "Rainfall Forecasting Based on Surface Data of Chennai Region Using Artificial Neural Networks",
    "source": "Smart Scholars DOI Registry",
    "container-title": "Journal of Science & Technology",
    "issued": {
        "date-parts": [
            [
                2020,
                9,
                24
            ]
        ]
    },
    "issue": "Volume 5",
    "page": "26-36",
    "publisher": "Longman Publishers",
    "language": "en",
    "abstract": "In this study, we developed user friendly rainfall forecasting system based on Back propagation Neural Network using MATLAB 7.10 to forecast Hourly rainfall in Chennai region. The dataset of 31488 samples has been collected from Nungambakkam Meteorological Station, Chennai for the period of 2005 to 2015. The data was organized into day-wise hourly recordings as well as day-wise, maximum, minimum, average data of Relative Humidity (RH), Temperature, Pressure and Wind Speed along with Rainfall data. The collected dataset has been used both for training and for testing the data. The developed system gives more accuracy of 94.8197% when the training data set is 55% and the testing data set is 45% with least Mean Squared Error (MSE) value 0.012437.",
    "ISSN": "2456-5660"
}

From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2020.v5.i6.pp26-36 gives all four in one JSON answer.