Comparative Study of Different Discrete Wavelet Based Neural Network Models for long term Drought Forecasting

Djerbouai, Salim; Souag-Gamane, Doudja; Ferhati, Ahmed; Djoukbala, Omar; Mostafa, Dougha; Oussama, Benselama; Hasbaia, Mahmoud

doi:10.1007/s11269-023-03432-0

Cited by 11 publications

(4 citation statements)

References 45 publications

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“…Methods such as the discrete Fourier transform (Bracewell and Bracewell, 1986), the discrete wavelet transform (Daubechies, 1992), and the Z-Transform (Foster, 1996) have been used to analyze time series. For deep learning models, similar methods have been used in the preprocessing steps (Cui et al, 2016;Yuan et al, 2017;Salim et al, 2023) or as part of neural networks (Koutnik et al, 2014;Lee-Thorp et al, 2021). Most of these models focus on univariate time series data and cannot be used directly on multivariate and multirate time series data.…”

Section: Related Workmentioning

confidence: 99%

Impact of the COVID-19 Pandemic on the Academic Community Results from a survey conducted at University of Massachusetts Amherst

Deznabi

Motahar

Sarvghad

et al. 2021

Digit. Gov.: Res. Pract.

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The COVID-19 pandemic has significantly impacted academic life in the United States and beyond. To gain a better understanding of its impact on the academic community, we conducted a large-scale survey at the University of Massachusetts Amherst. We collected multifaceted data from students, staff, and faculty on several aspects of their lives, such as mental and physical health, productivity, and finances. All our respondents expressed mental and physical issues and concerns, such as increased stress and depression levels. Financial difficulties seem to have the most considerable toll on staff and undergraduate students, while productivity challenges were mostly expressed by faculty and graduate students. As universities face many important decisions with respect to mitigating the effects of this pandemic, we present our findings with the intent of shedding light on the challenges faced by various academic groups in the face of the pandemic, calling attention to the differences between groups. We also contribute a discussion highlighting how the results translate to policies for the effective and timely support of the categories of respondents who need them most. Finally, the survey itself, which includes conditional logic allowing for personalized questions, serves as a template for further data collection, facilitating a comparison of the impact on campuses across the United States.

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Section: Related Workmentioning

confidence: 99%

Impact of the COVID-19 Pandemic on the Academic Community Results from a survey conducted at University of Massachusetts Amherst

Deznabi

Motahar

Sarvghad

et al. 2021

Digit. Gov.: Res. Pract.

View full text Add to dashboard Cite

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“…He benefited from a series of MI algorithms involving SVM, Gaussian Processes Regression (GPR), Regression Tree (RT), Ensembles of Trees (ET), extreme Gradient Boosting (XGBOOST) and WT in his study. Performing coupled WT and artificial neural networks (WANN) models, Salim et al, (2023) used 39 discrete mother wavelets derived from five families including Haar, Daubechies, Symlets, Coiflets and the discrete approximation of Meyer. They compared all discrete mother wavelets to each other and addressed that Meyer have the best forecast performance.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Forecasting Drought by Means of Various Machine Learning Algorithms and Wavelet Transformation

Tuğrul,

HINIS

2024

Preprint

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Defined as a decrease in average rainfall amounts, drought is one of the most insidious natural disasters. When it starts, people may not be aware of it, that's why droughts are difficult to monitor. Scientists have long been working to predict and monitor droughts. For this purpose, they have developed many methods such as drought indices one of which Standardized Precipitation Index (SPI) is. In this study, SPI to detect droughts and machine learning algorithms, support vector machines (SVM), artificial neural networks (ANN), random forest (RF), decision tree (DT), frequently used in the literature to predict droughts and 3 different statistical methods: correlation coefficient (r), Root Mean-square Error (RMSE), Nash-Sutcliffe Efficiency (NSE) Coefficient to investigate model performance values were used. Wavelet analysis was also applied to improve model performances. Konya closed basin located in the middle of Türkiye in terms of location and is among the leading regions of Turkey in terms of grain is one of the regions most affected by droughts in Türkiye. One of the most important water resources of the region is the Apa dam. It provides water to many fields which fertile land in its vicinity and is affected by droughts. Therefore, this region was determined as the study area. Meteorological data, total monthly precipitation, that could represent the region were obtained between 1955 and 2020 from general directorate of state water works and general directorate of meteorology. The results show that among the models analyzed with machine learning algorithms, the best results were obtained from M04 model whose input structure was created from SPI, different times steps, data delayed up to 5 months and total monthly precipitation data for time t-1. Among machine learning algorithms, SVM has achieved the most successful results in not only without wavelet transform (WT) but also with WT. Effective results were obtained from M04 in which SVM with WT was used (NSE = 0.9942, RMSE = 0.0764, R = 0.9971).

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“…The wavelet (WT) decomposition method's capability to decompose the non-stationary hydrological time series into sub-series represents an efficient approach for interpreting hydrological processes (Ghamariadyan & Imteaz, 2021). By breaking down the original time series into several sub-series, the WT decomposition method enhances the predictive capability by extracting valuable information at different scales (Onderka & Chudoba, 2018;Shoaib et al, 2019;Salim et al, 2023). In the context of r-r prediction, the hybrid WANN (WT-ANN) method has gained prominence.…”

Section: Introductionmentioning

confidence: 99%

Multi-Step-Ahead Rainfall-Runoff Modeling: Decision Tree-Based Clustering for Hybrid Wavelet Neural- Networks Modeling

Molajou,

Nourani,

Tajbakhsh

et al. 2024

Preprint

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This paper introduces a novel hybrid approach for predicting the rainfall-runoff (r-r) phenomenon across different data division scenarios (50%-50%, 60%-40%, and 75%-25%) within two distinct watersheds, encompassing both monthly and daily scales. Additionally, the effectiveness of this newly proposed hybrid method is evaluated in multi-step ahead prediction (MSAP) scenarios. The proposed method comprises three primary steps. Initially, to address the non-stationarity of the runoff and rainfall time series, these series are decomposed into multiple sub-time series using the wavelet (WT) decomposition method. Subsequently, in the second step, the decomposed sub-series are utilized as input data for the M5 model tree, a decision tree-based model. The M5 model tree classifies the samples of decomposed runoff and rainfall time series into distinct classes. Finally, each class is modeled using an artificial neural network (ANN). The results demonstrate the superior efficiency of the proposed WT-M5-ANN method compared to other available hybrid methods. Specifically, the calculated R2 was 0.93 for the proposed WT-M5-ANN method, whereas it was 0.89 and 0.81 for the WT-ANN and WT-M5 methods, respectively, for the Lobbs Hole Creek watershed at the daily scale.

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Comparative Study of Different Discrete Wavelet Based Neural Network Models for long term Drought Forecasting

Cited by 11 publications

References 45 publications

Impact of the COVID-19 Pandemic on the Academic Community Results from a survey conducted at University of Massachusetts Amherst

Impact of the COVID-19 Pandemic on the Academic Community Results from a survey conducted at University of Massachusetts Amherst

Improvement of Forecasting Drought by Means of Various Machine Learning Algorithms and Wavelet Transformation

Multi-Step-Ahead Rainfall-Runoff Modeling: Decision Tree-Based Clustering for Hybrid Wavelet Neural- Networks Modeling

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