Comparing Performance of ANN and SVM Methods for Regional Flood Frequency Analysis in South-East Australia

Zalnezhad, Amir; Rahman, Ataur; Nasiri, Nastaran; Vafakhah, Mehdi; Samali, Bijan; Ahamed, Farhad

doi:10.3390/w14203323

Cited by 13 publications

(6 citation statements)

References 60 publications

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“…Although the REm values are not remarkably different across the four regression models, LASSO has the smallest REm values overall. The REm values for LASSO are 37%, 44%, 43%, 44%, 43% and 46%, which are generally smaller than similar RFFA studies, such as that by Zalnezhad et al [21], who reported REm values of 42%, 33%, 36%, 40%, 44% and 54% for ARIs of 2, 5, 10, 20, 50 and 100 years, respectively, for an artificial neural networks (ANN)-AM-based RFFA model for south-east Australia. Zalnezhad et al [21] reported median QPred/QObs ratio values in the range of 0.94 to 1.57, which are very close to 1.00 in this study.…”

Section: Resultscontrasting

confidence: 59%

“…The REm values for LASSO are 37%, 44%, 43%, 44%, 43% and 46%, which are generally smaller than similar RFFA studies, such as that by Zalnezhad et al [21], who reported REm values of 42%, 33%, 36%, 40%, 44% and 54% for ARIs of 2, 5, 10, 20, 50 and 100 years, respectively, for an artificial neural networks (ANN)-AM-based RFFA model for south-east Australia. Zalnezhad et al [21] reported median QPred/QObs ratio values in the range of 0.94 to 1.57, which are very close to 1.00 in this study. The REm values for LASSO are also smaller than those recom- The RE m (Equation ( 4)) for the four regression models for all six ARIs are shown in Table 4.…”

Section: Resultscontrasting

confidence: 59%

“…The GDR divides the coastal region of south-eastern Australia into coastal and inland plains. The rationale for including both areas is based on the previous studies of Ali and Rahman [20] and Zalnezhad et al [21], which suggest considering both inland and coastal as a single region for RFFA. A total of seven catchment characteristics are adopted as independent variables in this study.…”

Section: Study Area and Datamentioning

confidence: 99%

See 2 more Smart Citations

Peaks-Over-Threshold-Based Regional Flood Frequency Analysis Using Regularised Linear Models

Pan,

Yildirim,

Rahman

et al. 2023

Water

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Regional flood frequency analysis (RFFA) is widely used to estimate design floods in ungauged catchments. Most of the RFFA techniques are based on the annual maximum (AM) flood model; however, research has shown that the peaks-over-threshold (POT) model has greater flexibility than the AM model. There is a lack of studies on POT-based RFFA techniques. This paper presents the development of POT-based RFFA techniques, using regularised linear models (least absolute shrinkage and selection operator, ridge regression and elastic net regression). The results of these regularised linear models are compared with multiple linear regression. Data from 145 stream gauging stations of south-east Australia are used in this study. A leave-one-out cross-validation is adopted to compare these regression models. It has been found that the regularised linear models provide quite accurate flood quantile estimates, with a median relative error in the range of 37 to 47%, which outperform the AM-based RFFA techniques currently recommended in the Australian Rainfall and Runoff guideline. The developed RFFA technique can be used to estimate flood quantiles in ungauged catchments in the study region.

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Section: Resultscontrasting

confidence: 59%

Section: Resultscontrasting

confidence: 59%

Section: Study Area and Datamentioning

confidence: 99%

See 1 more Smart Citation

Peaks-Over-Threshold-Based Regional Flood Frequency Analysis Using Regularised Linear Models

Pan,

Yildirim,

Rahman

et al. 2023

Water

Self Cite

View full text Add to dashboard Cite

show abstract

“…Zalnezhad et al compared the performance of SVM and ANN for analyzing flood frequency. 74 Based on independent tests, they found that the ANN method was superior to the SVM method with relative error values of 33 1). For some experimenters who do not have computer programming knowledge but need to apply AI technology to the application of enzyme engineering, simpler and easier-tounderstand models and data migration tools are required.…”

Section: Models and Algorithms For Ai In Lipase Technologymentioning

confidence: 99%

“…However, the support vector machine (SVM) always shows better accuracy than other classifiers due to its strong generalization ability and low computational complexity. Zalnezhad et al compared the performance of SVM and ANN for analyzing flood frequency . Based on independent tests, they found that the ANN method was superior to the SVM method with relative error values of 33–54% for the ANN model and 37–64% for the SVM model.…”

Section: Models and Algorithms For Ai In Lipase Technologymentioning

confidence: 99%

Artificial Intelligence Aided Lipase Production and Engineering for Enzymatic Performance Improvement

Ge,

Chen,

Qian

et al. 2023

J. Agric. Food Chem.

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With the development of artificial intelligence (AI), tailoring methods for enzyme engineering have been widely expanded. Additional protocols based on optimized network models have been used to predict and optimize lipase production as well as properties, namely, catalytic activity, stability, and substrate specificity. Here, different network models and algorithms for the prediction and reforming of lipase, focusing on its modification methods and cases based on AI, are reviewed in terms of both their advantages and disadvantages. Different neural networks coupled with various algorithms are usually applied to predict the maximum yield of lipase by optimizing the external cultivations for lipase production, while one part is used to predict the molecule variations affecting the properties of lipase. However, few studies have directly utilized AI to engineer lipase by affecting the structure of the enzyme, and a set of research gaps needs to be explored. Additionally, future perspectives of AI application in enzymes, including lipase engineering, are deduced to help the redesign of enzymes and the reform of new functional biocatalysts. This review provides a new horizon for developing effective and innovative AI tools for lipase production and engineering and facilitating lipase applications in the food industry and biomass conversion.

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Machine learning‐based regional flood frequency analysis of Indian watersheds

Mangukiya,

Kumar,

Sharma

2023

CLEAN Soil Air Water

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Estimates of the flood quantile for ungauged watersheds are crucial for water resources management but challenging due to the nonlinear complex hydrological system. For ungauged watersheds, estimating flood quantiles relies on various interdependent physiometeorological variables, many of which are not adequately considered in regional flood frequency analysis (RFFA). In this study, we utilized the random forest (RF) and support vector regression (SVR) algorithms, which can learn the nonlinear relationship between the physiometeorological variables and flood quantiles for RFFA. Thirteen physiometeorological variables that were not collectively employed before were used to estimate the 10‐year, 50‐year, and 100‐year return period flood quantiles (Q10, Q50, and Q100), respectively, for 39 watersheds spread across India. The RF and SVR models were trained on 29 (75%) watersheds to estimate individual flood quantiles and were subsequently tested on the remaining ten (25%) ungauged watersheds. The R2 achieved by RF is 0.862, 0.813, and 0.845, and SVR is 0.807, 0.793, and 0.789 for Q10, Q50, and Q100, respectively. Overall, the results indicate that RF can effectively learn the nonlinear relationships, while SVR with a linear kernel requires further improvement to estimate reliable flood quantiles. The study demonstrates that machine learning algorithms, with appropriate physiometeorological input datasets, can be used to estimate flood quantiles even in the sparse data region.

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Comparing Performance of ANN and SVM Methods for Regional Flood Frequency Analysis in South-East Australia

Cited by 13 publications

References 60 publications

Peaks-Over-Threshold-Based Regional Flood Frequency Analysis Using Regularised Linear Models

Peaks-Over-Threshold-Based Regional Flood Frequency Analysis Using Regularised Linear Models

Artificial Intelligence Aided Lipase Production and Engineering for Enzymatic Performance Improvement

Machine learning‐based regional flood frequency analysis of Indian watersheds

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