Data-Driven Forecasting and Modeling of Runoff Flow to Reduce Flood Risk Using a Novel Hybrid Wavelet-Neural Network Based on Feature Extraction

Heydari, Salimeh Malekpour; Aris, Teh Noranis Mohd; Yaakob, Razali; Hamdan, Hazlina

doi:10.3390/su132011537

Cited by 8 publications

(2 citation statements)

References 37 publications

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“…This is crucial for analyzing nonlinear and non-stationary economic and financial time series, which can interact differently on different time scales [26][27][28][29][30][31][32][33][34][35]. In connection with such undoubted advantages, methods for forecasting nonlinear non-stationary economic and financial time series based on wavelet packet transform and combined methods have recently been actively developed, including Wavelet Artificial Neural Networks (WANN), Wavelet Least-Squares Support Vector Machine (WLSSVM), and Multivariate Adaptive Regression Splines (MARS) [36][37][38][39][40][41][42][43][44][45][46]. Their results indicate a significant increase in the performance and accuracy of traditional time series forecasting models in combination with wavelet packet transform (WPT).…”

Section: Literature Reviewmentioning

confidence: 99%

Improving the Accuracy of Forecasting the TSA Daily Budgetary Fund Balance Based on Wavelet Packet Transforms

Karaev

Gorlova

Sedova

et al. 2022

Journal of Open Innovation: Technology, Market, and Complexity

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Improving the accuracy of cash flow forecasting in the TSA is the key to fulfilling government payment obligations, minimizing the cost of maintaining the cash reserve, providing the absence of outstanding debt accumulation, and ensuring investment in various financial instruments to obtain additional income. The article describes a method for improving the accuracy of forecasting a time series composed of daily budgetary fund balances in the TSA, based on its preliminary decomposition using a discrete wavelet packet transform of the Daubechies family. This makes it possible to increase the accuracy of traditional forecasting methods from 80% to more than 96%. The decomposition level varied from one to eight to minimize the mean absolute error and improve the forecasting accuracy. Calculations of statistical tests for adequacy confirm the effectiveness of the proposed method for improving forecasting accuracy. The scientific novelty of the proposed method for improving the forecasting accuracy of time series from daily budgetary fund balances in the TSA lies in proving the need for preliminary timeseries decomposition and subsequent construction of forecasts for the obtained parts, resulting in high forecasting accuracy. The result differs significantly from traditional econometric methods (ARIMA/SARIMA), characterized by a much lower accuracy (50–80%) and a decrease in forecasting accuracy with an increase in the forecast horizon. This article is novel, as it forms a new approach to solving the problem of increasing the efficiency of using budgetary funds, associated with improving the accuracy of forecasting daily budgetary fund balance in the TSA.

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Section: Literature Reviewmentioning

confidence: 99%

Improving the Accuracy of Forecasting the TSA Daily Budgetary Fund Balance Based on Wavelet Packet Transforms

Karaev

Gorlova

Sedova

et al. 2022

Journal of Open Innovation: Technology, Market, and Complexity

View full text Add to dashboard Cite

show abstract

“…At present, biometric identification technology is widely used in daily life. For example, nowadays, almost all cell phones have fingerprint unlocking and face unlocking, and when using a cell phone, more and more people no longer enter the unlocking password but press the fingerprint or use the face to the camera can be unlocked directly [14][15][16]. Gradually, the password unlocking method on cell phones has been replaced by two unlocking methods: fingerprint unlocking and face unlocking.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Criminal Suspect Characteristics with Application of Wavelet Neural Networks

Lei,

Huang

2023

Applied Mathematics and Nonlinear Sciences

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Crime incidents grow at a certain rate every year and become more complicated day by day, but at this stage, the development of the field of crime analysis and prediction is still lagging, so this paper applies a wavelet neural network to predict the characteristics of criminal suspects. Analyze the applicability of the WNN model, explore the principle of criminal suspect feature prediction, and study the basis of wavelet neural network analysis. Establish the face feature recognition framework, use the Morlet function and Mexican Hat function pair to optimize and improve the wavelet neural network, and construct the model evaluation index for evaluating the model prediction effect. Preprocessing the suspect feature point data by using the grayscale reflection in Haar features. Simulation experiments are used to analyze the performance of different algorithms and the prediction effect of criminal suspect features on wavelet neural networks. The proposed algorithm in this study is superior and has a lesser number of parameters than ResNet. From the loss curves of different algorithms, the algorithm proposed in this study has a faster-descending loss curve and a smaller loss rate in the epoch value of [100, 200] interval. The overall range of the evaluation indexes of the algorithms proposed in this study is above 88%, and the highest accuracy rate can reach 94.234%, which is a good performance of the algorithms and accurate prediction.

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A fusion-based framework for daily flood forecasting in multiple-step-ahead and near-future under climate change scenarios: a case study of the Kan River, Iran

Khajehali,

Safavi,

Nikoo

et al. 2024

Nat Hazards

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This study proposes a novel fusion framework for ood forecasting based on machine learning, statistical, and geostatistical models for daily multiple-step-ahead and near future under climate change scenarios. To do this, remote sensing precipitation data of ERA5, CHIRPS, and PERSIANN-CDR were utilized to ll the gap data of meteorological stations. Four Individual Machine Learning (IML) models, including Random Forest, Multiple-Layer Perceptron, Support Vector Machine, and Extreme Learning Machine were developed for twelve days ahead of stream ow modeling. Then, three fusion models, including Random Forest, Bayesian Model Averaging, and Bayesian Maximum Entropy were applied to combine the outputs of IML models. The proposed framework also was implemented to downscale the precipitation variable of three general climate models (GCMs) under SSP5-8.5 and SSP1-2.6 scenarios. The results indicated that individual models illustrated weak performance, especially in far steps ood forecasting, so it is necessary to utilize a fusion technique to improve the results. In the fusion step, the RF model indicated high e ciency compared to other fusion models. This technique also demonstrated an effective pro ciency in downscaling precipitation data of GCMs on a daily scale. Finally, ood forecasting model was developed based on the fusion framework in the near future (2020-2040) by using the precipitation data of two scenarios. We conclude that ood events based on both SSP5-8.5 and SSP1-2.6 will increase in the future in our case study. Also, the frequency evaluation shows that oods under SSP1-2.6 will occur about 10 percent more than SSP5-8.5 in the Kan river basin from 2020 to 2040.

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Data-Driven Forecasting and Modeling of Runoff Flow to Reduce Flood Risk Using a Novel Hybrid Wavelet-Neural Network Based on Feature Extraction

Cited by 8 publications

References 37 publications

Improving the Accuracy of Forecasting the TSA Daily Budgetary Fund Balance Based on Wavelet Packet Transforms

Improving the Accuracy of Forecasting the TSA Daily Budgetary Fund Balance Based on Wavelet Packet Transforms

Prediction of Criminal Suspect Characteristics with Application of Wavelet Neural Networks

A fusion-based framework for daily flood forecasting in multiple-step-ahead and near-future under climate change scenarios: a case study of the Kan River, Iran

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