Daily Flood Forecasts with Intelligent Data Analytic Models: Multivariate Empirical Mode Decomposition-Based Modeling Methods

Prasad, Ramendra; Charan, Dhrishna; Joseph, Lionel; Nguyen‐Huy, Thong; Deo, Ravinesh C.; Singh, Sanjay

doi:10.1007/978-981-15-5772-9_17

Cited by 4 publications

(3 citation statements)

References 89 publications

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“…Next, we describe the characteristics of these performance metrics, together with their mathematical formulation [12,13,32,50,[83][84][85][86][87][88][89].…”

Section: Evaluation Of Model Performancementioning

confidence: 99%

Hybrid Convolutional Neural Network-Multilayer Perceptron Model for Solar Radiation Prediction

et al. 2022

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Urgent transition from the dependence on fossil fuels towards renewable energies requires more solar photovoltaic power to be connected to the electricity grids, with reliable supply through accurate solar radiation forecasting systems. This study proposes an innovative hybrid method that integrates convolutional neural network (CNN) with multi-layer perceptron (MLP) to generate global solar radiation (GSR) forecasts. The CMLP model first extracts optimal topological and structural features embedded in predictive variables through a CNN-based feature extraction stage followed by an MLP-based predictive model to generate the GSR forecasts. Predictive variables from observed data and global climate models (GCM) are used to predict GSR at six solar farms in Queensland, Australia. A hybrid-wrapper feature selection method using a random forest-recursive feature elimination (RF-RFE) scheme is used to eradicate redundant predictor features to improve the proposed CMLP model efficiency. The CMLP model has been compared and bench-marked against seven artificial intelligence-based and seven temperature-based deterministic models, showing excellent performance at all solar energy study sites tested over daily, monthly, and seasonal scales. The proposed hybrid CMLP model should be explored as a viable modelling tool for solar energy monitoring and forecasting in real-time energy management systems.

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“…Next, we describe the characteristics of these performance metrics, together with their mathematical formulation [12,13,32,50,[83][84][85][86][87][88][89].…”

Section: Evaluation Of Model Performancementioning

confidence: 99%

Hybrid Convolutional Neural Network-Multilayer Perceptron Model for Solar Radiation Prediction

et al. 2022

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“…The flood indices, such as AWRI, SWAP, I F , and SAPI, are robust as they are designed to account for changes in antecedent or immediate past rainfall by employing a suitable time-dependent reduction function that accounts for the depletion of water resources through various hydrological processes. For example, the daily flood index, I F , applied in Australia [17,19,20], Iran [21], Bangladesh [22,23], and Fiji [6], has shown good performance in monitoring flood events on a daily scale. Despite its benefits, one primary weakness of I F and other indices, such as SPI, lies in their utilisation of daily, monthly, or annual accumulated rainfall data, which represent much longer timescales than what is required in a flash flood monitoring system.…”

Section: Introductionmentioning

confidence: 99%

Copula-Probabilistic Flood Risk Analysis with an Hourly Flood Monitoring Index

Chand,

Nguyen-Huy,

Deo

et al. 2024

Water

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Floods are a common natural disaster whose severity in terms of duration, water resource volume, peak, and accumulated rainfall-based damage is likely to differ significantly for different geographical regions. In this paper, we first propose a novel hourly flood index (SWRI24−hr−S) derived from normalising the existing 24-hourly water resources index (WRI24−hr−S) in the literature to monitor flood risk on an hourly scale. The proposed SWRI24−hr−S is adopted to identify a flood situation and derive its characteristics, such as the duration (D), volume (V), and peak (Q). The comprehensive result analysis establishes the practical utility of SWRI24−hr−S in identifying flood situations at seven study sites in Fiji between 2014 and 2018 and deriving their characteristics (i.e., D, V, and Q). Secondly, this study develops a vine copula-probabilistic risk analysis system that models the joint distribution of flood characteristics (i.e., D, V, and Q) to extract their joint exceedance probability for the seven study sites in Fiji, enabling probabilistic flood risk assessment. The vine copula approach, particularly suited to Fiji’s study sites, introduces a novel probabilistic framework for flood risk assessment. The results show moderate differences in the spatial patterns of joint exceedance probability of flood characteristics in different combination scenarios generated by the proposed vine copula approach. In the worst-case scenario, the probability of any flood event occurring where the flood volume, peak, and duration are likely to exceed the 95th-quantile value (representing an extreme flood event) is found to be less than 5% for all study sites. The proposed hourly flood index and the vine copula approach can be feasible and cost-effective tools for flood risk monitoring and assessment. The methodologies proposed in this study can be applied to other data-scarce regions where only rainfall data are available, offering crucial information for flood risk monitoring and assessment and for the development of effective mitigation strategies.

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“…Prediction of IF using artificial intelligence methods has developed rapidly in recent years. For example, Prasad et al (2021) to efficiently extract non-linear and complex compound connections from data (Ghimire et al, 2019). Additionally, DL algorithms are highly effective in extracting data attributes when handling enormous volumes of complicated data and possessing strong computational and sophisticated mapping capabilities (Gong et al, 2019).…”

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confidence: 99%

Real-time prediction of the week-ahead flood index using hybrid deep learning algorithms with synoptic climate mode indices

Ahmed

Farheen

Nguyen‐Huy

et al. 2023

Preprint

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This paper aims to propose a hybrid deep learning (DL) model that combines a convolutional neural network (CNN) with a bi-directional long-short term memory (BiLSTM) for week-ahead prediction of daily flood index (IF) for Bangladesh. The neighbourhood component analysis (NCA) is assigned for significant feature selection with synoptic-scale climatic indicators. The results successfully reveal that the hybrid CNN-BiLSTM model outperforms the respective benchmark models based on forecasting capability, as supported by a minimal mean absolute error and high-efficiency metrics. With respect to IF prediction, the hybrid CNN-BiLSTM model shows over 98% of the prediction errors were less than 0.015, resulting in a low relative error and superiority performance against the benchmark models in this study. The adaptability and potential utility of the suggested model may be helpful in subsequent flood monitoring and may also be beneficial to policymakers at the federal and state levels.

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Daily Flood Forecasts with Intelligent Data Analytic Models: Multivariate Empirical Mode Decomposition-Based Modeling Methods

Cited by 4 publications

References 89 publications

Hybrid Convolutional Neural Network-Multilayer Perceptron Model for Solar Radiation Prediction

Hybrid Convolutional Neural Network-Multilayer Perceptron Model for Solar Radiation Prediction

Copula-Probabilistic Flood Risk Analysis with an Hourly Flood Monitoring Index

Real-time prediction of the week-ahead flood index using hybrid deep learning algorithms with synoptic climate mode indices

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