Wavelet-ANN versus ANN-Based Model for Hydrometeorological Drought Forecasting

Abstract

Malaysia is one of the countries that has been experiencing droughts caused by a warming climate. This study considered the Standard Index of Annual Precipitation (SIAP) and Standardized Water Storage Index (SWSI) to represent meteorological and hydrological drought, respectively. The study area is the Langat River Basin, located in the central part of peninsular Malaysia. The analysis was done using rainfall and water level data over 30 years, from 1986 to 2016. Both of the indices were calculated in monthly scale, and two neural network-based models and two wavelet-based artificial neural network (W-ANN) models were developed for monthly droughts. The performance of the SIAP and SWSI models, in terms of the correlation coefficient (R), was 0.899 and 0.968, respectively. The application of a wavelet for preprocessing the raw data in the developed W-ANN models achieved higher correlation coefficients for most of the scenarios. This proves that the created model can predict meteorological and hydrological droughts very close to the observed values. Overall, this study helps us to understand the history of drought conditions over the past 30 years in the Langat River Basin. It further helps us to forecast drought and to assist in water resource management.

Statistical Analysis and Stochastic Modelling of Hydrological Extremes

“…Mahmud et al [12] 31 March 2018 2 4 1 1 Rhee and Yang [13] 14 June 2018 2 2 0 0 Khan et al [14] 27 July 2018 2 2 1 1 Mousavi et al [15] 16 October 2018 2 3 1 1 Amnatsan et al [16] 9 November 2018 3 3 0 0 Bafitlhile and Li [17] 6 January 2019 3 3 1 1 Pan et al [18] 22 January 2019 2 2 0 0 Ávila et al [19] 22 February 2019 4 5 2 1 Pham et al [20] 3 March 2019 3 3 1 1 Tung et al [21] 8 March 2019 2 2 0 0 Dawley et al [22] 5 April 2019 3 3 0 0 Zhang and Wang [23] 4 June 2019 2 5 0 0 Mehmood et al [24] 14 June 2019 3 5 0 0…”

“…To overcome the limitation of the sparse network, dynamically downscaled historical climate data from the Weather Research and Forecasting (WRF) model were used to train machine learning models instead of in-situ data as a reference. In another study, Khan et al [14] developed two artificial neural network (ANN)-based models and two wavelet-based artificial neural network (W-ANN) models for meteorological and hydrological droughts characterized by Standard Index of Annual Precipitation (SIAP) and Standardized Water Storage Index (SWSI), respectively.…”

Wavelet Packet-Genetic Programming: A New Model for Meteorological Drought Hindcasting

“…Despite the fact that the optimal set of lags leads to the reliable forecasts, inadequate or even more than required lags may result in weak or complex solutions, respectively. It might be identified either via trial and error method [28] or through the autocorrelation function (ACF) analysis of drought index [21]. However, it is a linear method and the values achieved are often too large.…”

Evaluation of drought events in various climatic conditions using data-driven models and a reliability-based probabilistic model

“…One of the methods used to predict the drought event situations is the Artificial Intelligence (AI) techniques which could detect efficiently general patterns/variations between drought indices and meteorological data (e.g., Ozger et al, 2011;Belayneh et al 2014;Abbot and Marohasy 2014;Salcedo-Sanz et al 2016). For instance, ANN (Djerbouai and Souag-Gamane 2016;Khan et al, 2018;Mulualem and Liou, 2020), ANFIS (Rahmati et al, 2020) and Support Vector Regression (SVR) (Deo et al, 2018;Dikshit et al, 2020) are the cases of Artificial Intelligence (AI) techniques which were employed to estimate time series modeling.…”