Forecasting Daily and Monthly Reference Evapotranspiration in the Aidoghmoush Basin Using Multilayer Perceptron Coupled with Water Wave Optimization

Sayyahi, Fatemeh; Farzin, Saeed; Karami, Hojat

doi:10.1155/2021/6683759

Cited by 10 publications

(5 citation statements)

References 40 publications

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“…In this study, Figure 5 revealed that the first four lags were selected as the optimum model input by avoiding multicollinearity, which is vital for accurate ETo estimation. This is in line with the findings of Roy et al [25] and Sayyahi et al [15], which applied various variable selection techniques to determine the best number of predictors in order to produce the best possible forecast performance.…”

Section: Discussionsupporting

confidence: 89%

Examination of Single- and Hybrid-Based Metaheuristic Algorithms in ANN Reference Evapotranspiration Estimating

Khairan,

Zubaidi,

Raza

et al. 2023

Sustainability

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Hydrological resource management, including crop watering and irrigation scheduling, relies on reliable estimates of reference evapotranspiration (ETo). However, previous studies of forecasting ETo have not dealt with comparing single and hybrid metaheuristic algorithms in much detail. This study aims to assess the efficiency of a novel methodology to simulate univariate monthly ETo estimates using an artificial neural network (ANN) integrated with the hybrid particle swarm optimisation–grey wolf optimiser algorithm (PSOGWO). Several state-of-the-art algorithms, including constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithms (CPSOCGSA), the slime mould algorithm (SMA), the marine predators algorithm (MPA) and the modified PSO algorithm were used to evaluate PSOGWO’s prediction accuracy. Monthly meteorological data were collected in Al-Kut City (1990 to 2020) and used for model training, testing and validation. The results indicate that pre-processing techniques can improve raw data quality and may also suggest the best predictors scenario. That said, all models can be considered efficient with acceptable simulation levels. However, the PSOGWO-ANN model slightly outperformed the other techniques based on several statistical tests (e.g., a coefficient of determination of 0.99). The findings can contribute to better management of water resources in Al-Kut City, an agricultural region that produces wheat in Iraq and is under the stress of climate change.

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Section: Discussionsupporting

confidence: 89%

Examination of Single- and Hybrid-Based Metaheuristic Algorithms in ANN Reference Evapotranspiration Estimating

Khairan,

Zubaidi,

Raza

et al. 2023

Sustainability

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“…Using the same dataset NSL-KDD, the detection accuracy of seven traditional machine learning algorithms [20][21][22][23][24][25][26][27][28][29][30][31][32][33] such as decision tree, Naive Bayes, Naive Bayes tree, random tree, random forest, support vector machine, multilayer perceptron, and the detection accuracy of the FCRNN-IDS model in the case of 2-class (normal, abnormal) and 5-class (normal, probe, Dod, R2L and U2R) were studied and compared.…”

Section: Comparative Experiments 1: Comparison With Traditional Machi...mentioning

confidence: 99%

An Intrusion Detection Method Based on Fully Connected Recurrent Neural Network

2022

Scientific Programming

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Now, the use of deep learning technology to solve the problems of the low multiclassification task detection accuracy and complex feature engineering existing in traditional intrusion detection technology has become a research hotspot. In all kinds of deep learning, recurrent neural networks (RNN) are very important. The RNN processes 41 feature attributes and maps them to a 122-dimensional high-dimensional feature space. To detect multiclassification tasks, this study proposes an intrusion detection method based on fully connected recurrent neural networks and compares its performance with previous machine learning methods on benchmark datasets. The research results show that the intrusion detection system (IDS) model based on fully connected recurrent neural network is very suitable for classification of intrusion detection. Classification methods, especially in multiclassification tasks, have high detection accuracy, significantly improve the detection performance of detection attacks and DoS attacks, and it provides a new research direction for the future attempts of intrusion detection methods for industrial control systems.

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“…Wind waves are the most important waves observed at sea and have the greatest impact on human activities in the marine environment; therefore, when it comes to forecasting waves for engineering purposes, mainly wind waves are considered [9]. Although feld measurements are the most accurate way to obtain the wave parameters of any region, the feld measurement method alone will not be able to respond when determining waves in a wide area [10]. Today, using numerical models as an effcient tool for simulation and then studying complex natural processes open the way for many technical and engineering issues, including the state of the sea.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Hybrid Soft Computing Model’s Performance in Estimating Wave Height

Chen

Rashid

Sayed

et al. 2023

Advances in Civil Engineering

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In coastal and port engineering, wind-generated waves have always been a crucial, fundamental, and important topic. As a result, various methods for estimating wave parameters, including field measurement and numerical methods, have been proposed over time. This study evaluates the wave height at Sri-Lanka Hambantota Port using soft computing models such as Artificial Neural Networks (ANNs) and the M5 model tree (M5MT). In order to overcome its nonstationarity, the primary wave height time series were divided into subtime series using the wavelet transform. The collected subtime series were then utilized as input data for ANN and M5MT in order to determine the wave height. For the sake of the model performance, the daily wind and wave data from the Acoustic Wave and Current (AWAC) sensor for Hambantota Port in 2020 and Sanmen Bay in 2017 were used in this study. The training state utilizes 80% of the available data, while the test state uses 20%. The Root Mean Square Error (RMSE) of the ANN, M5, WANN, and Wavelet-M5 models in the Hambantota Port for the test stage are 0.12, 0.11, 0.04, and 0.06, respectively. While in Sanmen Bay, the RMSE of the ANN, M5, WANN, and Wavelet-M5 models for the test stage are 0.14, 0.16, 0.06, and 0.08, respectively. According to the findings of this study, the accuracy of WANN and Wavelet-M5 hybrid models in evaluating wave height is superior to that of classic ANN and M5MT, and it is recommended that WANN and Wavelet-M5 hybrid models be used to estimate wave height.

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Forecasting Daily and Monthly Reference Evapotranspiration in the Aidoghmoush Basin Using Multilayer Perceptron Coupled with Water Wave Optimization

Cited by 10 publications

References 40 publications

Examination of Single- and Hybrid-Based Metaheuristic Algorithms in ANN Reference Evapotranspiration Estimating

Examination of Single- and Hybrid-Based Metaheuristic Algorithms in ANN Reference Evapotranspiration Estimating

An Intrusion Detection Method Based on Fully Connected Recurrent Neural Network

Evaluation of Hybrid Soft Computing Model’s Performance in Estimating Wave Height

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