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- Title
Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Model.
- Authors
Ali, Zulifqar; Hussain, Ijaz; Faisal, Muhammad; Nazir, Hafiza Mamona; Hussain, Tajammal; Shad, Muhammad Yousaf; Mohamd Shoukry, Alaa; Hussain Gani, Showkat
- Abstract
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.
- Subjects
DROUGHTS &; the environment; ARTIFICIAL neural networks; ENVIRONMENTAL monitoring; COMPUTER algorithms; DATA analysis
- Publication
Advances in Meteorology, 2017, p1
- ISSN
1687-9309
- Publication type
Article
- DOI
10.1155/2017/5681308