Drought prediction using hybrid soft-computing methods for semi-arid region
- PDF / 1,417,441 Bytes
- 9 Pages / 595.276 x 790.866 pts Page_size
- 47 Downloads / 189 Views
ORIGINAL ARTICLE
Drought prediction using hybrid soft‑computing methods for semi‑arid region Eyyup Ensar Başakın1 · Ömer Ekmekcioğlu1 · Mehmet Özger1 Received: 29 July 2020 / Accepted: 8 October 2020 © Springer Nature Switzerland AG 2020
Abstract Drought is one of the most significant natural disaster and prediction of drought is a key aspect in effective management of water resources and reducing the effect of a drought with preliminary studies plays significant role. In this study, we predicted one of the meteorological drought indices, the self-calibrated Palmer Drought Severity Index (sc-PDSI), values for Adana, Turkey. First, we used adaptive neuro fuzzy inference system (ANFIS) as a standalone technique to predict sc-PDSI. Second, we used empirical mode decomposition (EMD) as a pre-processing technique to decompose the sc-PDSI time series into the sub-series and applied ANFIS to each sub-series. Following the prediction, results are summed each other and final prediction of the hybrid EMD-ANFIS method is obtained. Within the scope of the study, 1, 3and 6-months lead time sc-PDSI values are predicted. We utilized the mean square error (MSE) and Nash–Sutcliffe efficiency coefficient (NSE) as performance indicators in order to perform statistical evaluation. For ANFIS, we obtained NSE = 0.52 and NSE = 0.17 for 3-month and 6-month lead times, respectively. Also, NSE values are obtained as 0.81 and 0.77 for the hybrid model in 3-month and 6-month lead time predictions, respectively. The results revealed that the hybrid EMD-ANFIS model outperforms the standalone ANFIS model. Also, the predicted and actual sc-PDSI series investigated according to the statistical distributions. At last, error histograms of both predicted and actual series are compared according to the Kolmogorov–Smirnov test and the p values are calculated. The results illustrated the predictions are statistically significant. Keywords Self-calibrated PDSI · Drought · Fuzzy logic · Prediction · EMD
Introduction Drought can be defined as a lack of water resources due to decreasing rainfall and a natural disaster that interferes with the natural life process. The biggest difference that distinguishes drought from other natural disasters is that drought occurs slowly. The most important factors causing drought can be stated as global warming and climate change. On the other hand, water resources are adversely affected by drought. Lack of water causes not only negative effects in * Eyyup Ensar Başakın [email protected] Ömer Ekmekcioğlu [email protected] Mehmet Özger [email protected] 1
Faculty of Civil Engineering, Hydraulics and Water Resource Engineering Division, Istanbul Technical University, Istanbul 34469, Turkey
natural life chain but also regional migration. Thus, measures to manage water resources can be taken much more easily, if probable drought events can be identified. Drought indices are obtained by empirical methods and mathematical models (Hosseini-Moghari et al. 2017), as well. In this context, a great deal of mode
Data Loading...
