Abstract
A noticeable increase in drought frequency and severity has been observed across the globe due to climate change, which attracted scientists in development of drought prediction models for mitigation of impacts. Droughts are usually monitored using drought indices (DIs), most of which are probabilistic and therefore, highly stochastic and non-linear. The current research investigated the capability of different versions of relatively well-explored machine learning (ML) models including random forest (RF), minimum probability machine regression (MPMR), M5 Tree (M5tree), extreme learning machine (ELM) and online sequential-ELM (OSELM) in predicting the most widely used DI known as standardized precipitation index (SPI) at multiple month horizons (i.e., 1, 3, 6 and 12). Models were developed using monthly rainfall data for the period of 1949–2013 at four meteorological stations namely, Barisal, Bogra, Faridpur and Mymensingh, each representing a geographical region of Bangladesh which frequently experiences droughts. The model inputs were decided based on correlation statistics and the prediction capability was evaluated using several statistical metrics including mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R), Willmott’s Index of agreement (WI), Nash Sutcliffe efficiency (NSE), and Legates and McCabe Index (LM). The results revealed that the proposed models are reliable and robust in predicting droughts in the region. Comparison of the models revealed ELM as the best model in forecasting droughts with minimal RMSE in the range of 0.07–0.85, 0.08–0.76, 0.062–0.80 and 0.042–0.605 for Barisal, Bogra, Faridpur and Mymensingh, respectively for all the SPI scales except one-month SPI for which the RF showed the best performance with minimal RMSE of 0.57, 0.45, 0.59 and 0.42, respectively.
Highlights
A noticeable increase in drought frequency and severity has been observed across the globe due to climate change, which attracted scientists in development of drought prediction models for mitigation of impacts
Drought prediction for Bangladesh was conducted in this study using the standardized precipitation index (SPI) for different time-scales (1, 3, 6, 9 and 12 months) at four meteorological stations distributed over the country (e.g. Barisal, Bogra, Faridpur and Mymensingh)
The results revealed that random forest (RF) is the best predictive model for SPI-1 ( root mean square error (RMSE) = 0.43−0.54 ) at all stations while for the other scales of SPI such as SPI-3 ( RMSE = 0.2−0.72 ), SPI-6 ( RMSE = 0.09−0.22 ) and SPI-12 ( RMSE = 0.03−0.08 ) both extreme learning machine (ELM) and online sequential-ELM (OSELM) showed superiority compared to others
Summary
A noticeable increase in drought frequency and severity has been observed across the globe due to climate change, which attracted scientists in development of drought prediction models for mitigation of impacts. Models were developed using monthly rainfall data for the period of 1949–2013 at four meteorological stations namely, Barisal, Bogra, Faridpur and Mymensingh, each representing a geographical region of Bangladesh which frequently experiences droughts. Drought is a natural disaster that affects society and the environment frequently[1,2] It significantly influences water resources availability, agricultural production, environmental health and socio-economy of a region[3,4]. Meteorological droughts occur due to deficiency of precipitation from the average. It is the initiator of all other kinds of droughts and most widely studies for monitoring d roughts[10]. The large variability of precipitation on the deficit side indicates droughts The slowly emerging characteristics of droughts causes a challenge in determining and modeling of drought duration, intensity, severity, spatial extent and inter-arrival period[21,22]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
