Drought prediction using co-active neuro-fuzzy inference system, validation, and uncertainty analysis (case study: Birjand, Iran)

Abstract

This work aims to assess the capability of co-active neuro-fuzzy inference system (CANFIS) for drought forecasting of Birjand, Iran through the combination of global climatic signals with rainfall and lagged values of Standardized Precipitation Index (SPI) index. Using stepwise regression and correlation analyses, the signals NINO 1+2, NINO 3, Multivariate Enso Index, Tropical Southern Atlantic index, Atlantic Multi-decadal Oscillation index, and NINO 3.4 were recognized as the effective signals on the drought event in Birjand. Based on the results from stepwise regression analysis and regarding the processor limitations, eight models were extracted for further processing by CANFIS. The metrics P-factor and D-factor were utilized for uncertainty analysis, based on the sequential uncertainty fitting algorithm. Sensitivity analysis showed that for all models, NINO indices and rainfall variable had the largest impact on network performance. In model 4 (as the model with the lowest error during training and testing processes), NINO 1+2(t-5) with an average sensitivity of 0.7 showed the highest impact on network performance. Next, the variables rainfall, NINO 1+2(t), and NINO 3(t-6) with the average sensitivity of 0.59, 0.28, and 0.28, respectively, could have the highest effect on network performance. The findings based on network performance metrics indicated that the global indices with a time lag represented a better correlation with El Niño Southern Oscillation (ENSO). Uncertainty analysis of the model 4 demonstrated that 68 % of the observed data were bracketed by the 95PPU and D-Factor value (0.79) was also within a reasonable range. Therefore, the fourth model with a combination of the input variables NINO 1+2 (with 5 months of lag and without any lag), monthly rainfall, and NINO 3 (with 6 months of lag) and correlation coefficient of 0.903 (between observed and simulated SPI) was selected as the most accurate model for drought forecasting using CANFIS in the climatic region of Birjand.

Scientometric Analysis-Based Review for Drought Modelling, Indices, Types, and Forecasting Especially in Asia

“…Dynamic drought simulations rely heavily on real-time remote-sensing data. A methodology for acquiring accurate information about items, places, or phenomena from afar or without direct contact with the ground is known as remote sensing [33][34][35][36]. Drought-related climatological variables may be monitored using remote-sensing measurements, and it is possible to utilize these to research the effect of drought on ecosystems.…”

Drought index prediction using advanced fuzzy logic model: Regional case study over Kumaon in India

“…For instance, there are studies conducted on the SPI prediction using various versions of AI models [40,[51][52][53][54][55]. Memarian et al [56] applied the CANFIS model to predict the meteorological drought in Birjand, Iran using global climatic indicators and lagged values of SPI. They found a better predictive capability of the CANFIS model in the study region.…”

Viability of the advanced adaptive neuro-fuzzy inference system model on reservoir evaporation process simulation: case study of Nasser Lake in Egypt

“…On the other hand, the error is circulated to both the MFs and the employing network. The first layer contains several nodes and handling elements where, in each node, the membership class for a fuzzy set (R 1 , R 2 , T 1 , T 2 ) is quantified (Allawi, Jaafar, Mohamad Hamzah, Mohd, et al, 2018;Memarian, Pourreza Bilondi, & Rezaei, 2016).…”