Forecasting of extreme flood events using different satellite precipitation products and wavelet-based machine learning methods

Abstract

An accurate and timely forecast of extreme events can mitigate negative impacts and enhance preparedness. Real-time forecasting of extreme flood events with longer lead times is difficult for regions with sparse rain gauges, and in such situations, satellite precipitation could be a better alternative. Machine learning methods have shown promising results for flood forecasting with minimum variables indicating the underlying nonlinear complex hydrologic system. Integration of machine learning methods in extreme event forecasting motivates us to develop reliable flood forecasting models that are simple, accurate, and applicable in data scare regions. In this study, we develop a forecasting method using the satellite precipitation product and wavelet-based machine learning models. We test the proposed approach in the flood-prone Vamsadhara river basin, India. The validation results show that the proposed method is promising and has the potential to forecast extreme flood events with longer lead times in comparison with the other benchmark models.

Multiscale Spatiotemporal Analysis of Extreme Events in the Gomati River Basin, India

“…where W f (a, b) is the transform; a and b are the scale parameter and translation parameter, respectively; ψ * is the complex conjugate and t is the time scale [42].…”

“…Figure 6 shows the wavelet power spectrum for the climate indices, in which contours enclosing light yellow regions have larger power. The cone of influence is represented by a V shape with a black line to distinguish between non-significant periodic characteristics (edge effect artefacts) and significant periodic characteristics (within the cone of influence) at 95% confidence level [42]. Additionally, the global power spectrum and its statistical significance are depicted beside each climate index.…”

Investigation of satellite precipitation product driven rainfall-runoff model using deep learning approaches in two different catchments of India

“…In recent decades, there is a rise in remote sensing data to analyze various metrological parameters like temperature, precipitation, humidity, and several others. Some of the recent successful applications of remote sensing data included the works of Yeditha et al (2020) and Valipour & Bateni (2021). In the case of precipitation, due to an increase in various satellitederived precipitation products and having advantages of easy availability in real-time and high temporal and spatial coverages, they are being used as alternatives for ground-based gauged data sets.…”

“…A multitude number of studies have been reported on the utility of these SPPs for applications such as hydrological modelling, water balance (Su et al 2008;Zhang et al 2015;Xu et al 2017;Vu et al 2018;Gadhawe et al 2021), drought monitoring (Lai et al 2019), streamflow modelling (Munzimi et al 2019;Sulugodu & Deka 2019;Yeditha et al 2020), and water quality modelling (Ali & Shahbaz 2020). Indeed, a large number of studies have been carried out to understand the application of satellite precipitation and their evaluation (Janowiak et al 2004;Nair et al 2009;Rahman et al 2009;Bitew & Gebremichael 2011;Meng et al 2014;Prakash & Gairola 2014;Prakash et al 2015;Tang et al 2016;Li et al 2018;Wei et al 2018).…”

“…There are a significant number of studies (Kisi 2008;Islam 2010;Yaseen et al 2015;Couta et al 2019;Ding et al 2019;Ali & Shahbaz 2020;Kasi et al 2020;Wagena et al 2020;Yadav et al 2020;Yeditha et al 2020) reporting the efficiency of machine learning methods such as artificial neural networks (ANN), extreme learning machines (ELM) (Huang et al 2006), and long short-term memory (LSTM) (Kratzert et al 2019) models based on the rain gauge data for rainfall-runoff applications. Several studies (Huang et al 2006;Atiquzzaman & Kandasamy 2016;Sulugodu & Deka 2019) showed that ELM models outperformed ANN, SVR, and GRNN in terms of modelling accuracy and computation time.…”