Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Model
These days human beings are facing many environmental challenges due to frequently occurring drought hazards. It may have an effect on the country’s environment, the community, and industries. Several adverse impacts of drought hazard are continued in Pakistan, including other hazards. However, earl...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2017/5681308 |
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| author | Zulifqar Ali Ijaz Hussain Muhammad Faisal Hafiza Mamona Nazir Tajammal Hussain Muhammad Yousaf Shad Alaa Mohamd Shoukry Showkat Hussain Gani |
| author_facet | Zulifqar Ali Ijaz Hussain Muhammad Faisal Hafiza Mamona Nazir Tajammal Hussain Muhammad Yousaf Shad Alaa Mohamd Shoukry Showkat Hussain Gani |
| author_sort | Zulifqar Ali |
| collection | DOAJ |
| description | These days human beings are facing many environmental challenges due to frequently occurring drought hazards. It may have an effect on the country’s environment, the community, and industries. Several adverse impacts of drought hazard are continued in Pakistan, including other hazards. However, early measurement and detection of drought can provide guidance to water resources management for employing drought mitigation policies. In this paper, we used a multilayer perceptron neural network (MLPNN) algorithm for drought forecasting. We applied and tested MLPNN algorithm on monthly time series data of Standardized Precipitation Evapotranspiration Index (SPEI) for seventeen climatological stations located in Northern Area and KPK (Pakistan). We found that MLPNN has potential capability for SPEI drought forecasting based on performance measures (i.e., Mean Average Error (MAE), the coefficient of correlation (R), and Root Mean Square Error (RMSE)). Water resources and management planner can take necessary action in advance (e.g., in water scarcity areas) by using MLPNN model as part of their decision-making. |
| format | Article |
| id | doaj-art-b51407ffafe04cb49da8df1e8a240c5e |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-b51407ffafe04cb49da8df1e8a240c5e2025-08-20T03:39:22ZengWileyAdvances in Meteorology1687-93091687-93172017-01-01201710.1155/2017/56813085681308Forecasting Drought Using Multilayer Perceptron Artificial Neural Network ModelZulifqar Ali0Ijaz Hussain1Muhammad Faisal2Hafiza Mamona Nazir3Tajammal Hussain4Muhammad Yousaf Shad5Alaa Mohamd Shoukry6Showkat Hussain Gani7Department of Statistics, Quaid-i-Azam University, Islamabad, PakistanDepartment of Statistics, Quaid-i-Azam University, Islamabad, PakistanFaculty of Health Studies, University of Bradford, Bradford BD7 1DP, UKDepartment of Statistics, Quaid-i-Azam University, Islamabad, PakistanDepartment of Statistics, COMSATS Institute of Information Technology, Lahore, PakistanDepartment of Statistics, Quaid-i-Azam University, Islamabad, PakistanArriyadh Community College, King Saud University, Riyadh, Saudi ArabiaCollege of Business Administration, King Saud University, Muzahimiyah, Saudi ArabiaThese days human beings are facing many environmental challenges due to frequently occurring drought hazards. It may have an effect on the country’s environment, the community, and industries. Several adverse impacts of drought hazard are continued in Pakistan, including other hazards. However, early measurement and detection of drought can provide guidance to water resources management for employing drought mitigation policies. In this paper, we used a multilayer perceptron neural network (MLPNN) algorithm for drought forecasting. We applied and tested MLPNN algorithm on monthly time series data of Standardized Precipitation Evapotranspiration Index (SPEI) for seventeen climatological stations located in Northern Area and KPK (Pakistan). We found that MLPNN has potential capability for SPEI drought forecasting based on performance measures (i.e., Mean Average Error (MAE), the coefficient of correlation (R), and Root Mean Square Error (RMSE)). Water resources and management planner can take necessary action in advance (e.g., in water scarcity areas) by using MLPNN model as part of their decision-making.http://dx.doi.org/10.1155/2017/5681308 |
| spellingShingle | Zulifqar Ali Ijaz Hussain Muhammad Faisal Hafiza Mamona Nazir Tajammal Hussain Muhammad Yousaf Shad Alaa Mohamd Shoukry Showkat Hussain Gani Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Model Advances in Meteorology |
| title | Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Model |
| title_full | Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Model |
| title_fullStr | Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Model |
| title_full_unstemmed | Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Model |
| title_short | Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Model |
| title_sort | forecasting drought using multilayer perceptron artificial neural network model |
| url | http://dx.doi.org/10.1155/2017/5681308 |
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