Prediction Of Monthly Rainfall In Kirkuk Using Artificial Neural Network And Time Series Models
One of the major problems in water resources management is the rainfall forecasting. With the effect of rainfall on water resources as a foregone conclusion, more accurate prediction of rainfall would enable more efficient utilization of water resources and power generation. On the other hand, cl...
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| Format: | Article |
| Language: | Arabic |
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Mustansiriyah University/College of Engineering
2014-01-01
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| Series: | Journal of Engineering and Sustainable Development |
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| Online Access: | https://jeasd.uomustansiriyah.edu.iq/index.php/jeasd/article/view/855 |
| _version_ | 1811231207604617216 |
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| author | Shaymaa Abdul Muttaleb Alhashimi |
| author_facet | Shaymaa Abdul Muttaleb Alhashimi |
| author_sort | Shaymaa Abdul Muttaleb Alhashimi |
| collection | DOAJ |
| description |
One of the major problems in water resources management is the rainfall forecasting. With the effect of rainfall on water resources as a foregone conclusion, more accurate prediction of rainfall would enable more efficient utilization of water resources and power generation. On the other hand, climate and rainfall are highly non-linear and complicated phenomena, which require non-linear mathematical modeling and simulation for accurate prediction. One of the non-linear techniques being recently used for rainfall forecasting is the Artificial Neural Networks (ANN) approach which has the ability of mapping between input and output patterns without a prior knowledge of the system being modeled. In this study, three rainfall prediction models were developed and implemented based on past observations such as time series models based on autoregressive integrated moving average (ARIMA),Artificial Neural Network ANN model and Multi Linear Regression MLR model. A Feed Forward Neural Network FFNN model was applied to predict the rainfall on monthly basis. In order to evaluate the performance of three models, statistical parameters were used to make the comparison between these models. These parameters include the correlation coefficient (R) and Root Mean Square Errors(RMSE). The data set that has been used in this study includes monthly measurements for the rainfall, mean temperature, wind speed and relative humidity from year ...
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| first_indexed | 2024-04-12T10:41:26Z |
| format | Article |
| id | doaj.art-722962fd20ca4488a750aafc9ef0a200 |
| institution | Directory Open Access Journal |
| issn | 2520-0917 2520-0925 |
| language | Arabic |
| last_indexed | 2024-04-12T10:41:26Z |
| publishDate | 2014-01-01 |
| publisher | Mustansiriyah University/College of Engineering |
| record_format | Article |
| series | Journal of Engineering and Sustainable Development |
| spelling | doaj.art-722962fd20ca4488a750aafc9ef0a2002022-12-22T03:36:35ZaraMustansiriyah University/College of EngineeringJournal of Engineering and Sustainable Development2520-09172520-09252014-01-01181Prediction Of Monthly Rainfall In Kirkuk Using Artificial Neural Network And Time Series ModelsShaymaa Abdul Muttaleb Alhashimi0Transportation and Highway Engineering Department, Al-Mustansiriyah University, Baghdad, Iraq One of the major problems in water resources management is the rainfall forecasting. With the effect of rainfall on water resources as a foregone conclusion, more accurate prediction of rainfall would enable more efficient utilization of water resources and power generation. On the other hand, climate and rainfall are highly non-linear and complicated phenomena, which require non-linear mathematical modeling and simulation for accurate prediction. One of the non-linear techniques being recently used for rainfall forecasting is the Artificial Neural Networks (ANN) approach which has the ability of mapping between input and output patterns without a prior knowledge of the system being modeled. In this study, three rainfall prediction models were developed and implemented based on past observations such as time series models based on autoregressive integrated moving average (ARIMA),Artificial Neural Network ANN model and Multi Linear Regression MLR model. A Feed Forward Neural Network FFNN model was applied to predict the rainfall on monthly basis. In order to evaluate the performance of three models, statistical parameters were used to make the comparison between these models. These parameters include the correlation coefficient (R) and Root Mean Square Errors(RMSE). The data set that has been used in this study includes monthly measurements for the rainfall, mean temperature, wind speed and relative humidity from year ... https://jeasd.uomustansiriyah.edu.iq/index.php/jeasd/article/view/855rainfallartificial neural networktime series models |
| spellingShingle | Shaymaa Abdul Muttaleb Alhashimi Prediction Of Monthly Rainfall In Kirkuk Using Artificial Neural Network And Time Series Models Journal of Engineering and Sustainable Development rainfall artificial neural network time series models |
| title | Prediction Of Monthly Rainfall In Kirkuk Using Artificial Neural Network And Time Series Models |
| title_full | Prediction Of Monthly Rainfall In Kirkuk Using Artificial Neural Network And Time Series Models |
| title_fullStr | Prediction Of Monthly Rainfall In Kirkuk Using Artificial Neural Network And Time Series Models |
| title_full_unstemmed | Prediction Of Monthly Rainfall In Kirkuk Using Artificial Neural Network And Time Series Models |
| title_short | Prediction Of Monthly Rainfall In Kirkuk Using Artificial Neural Network And Time Series Models |
| title_sort | prediction of monthly rainfall in kirkuk using artificial neural network and time series models |
| topic | rainfall artificial neural network time series models |
| url | https://jeasd.uomustansiriyah.edu.iq/index.php/jeasd/article/view/855 |
| work_keys_str_mv | AT shaymaaabdulmuttalebalhashimi predictionofmonthlyrainfallinkirkukusingartificialneuralnetworkandtimeseriesmodels |