Regional flood frequency analysis (RFFA) is widely used to estimate design floods in ungauged catchments. Both linear and non-linear methods are adopted in RFFA. The development of the non-linear RFFA method Adaptive Neuro-fuzzy Inference System (ANFIS) using data from 181 gauged catchments in south-eastern Australia is presented in this study. Three different types of ANFIS models, Fuzzy C-mean (FCM), Subtractive Clustering (SC), and Grid Partitioning (GP) were adopted, and the results were compared with the Quantile Regression Technique (QRT). It was found that FCM performs better (with relative error (RE) values in the range of 38–60%) than the SC (RE of 44–69%) and GP (RE of 42–78%) models. The FCM performs better for smaller to medium ARIs (2 to 20 years) (ARI of five years having the best performance), and in New South Wales, over Victoria. In many aspects, the QRT and FCM models perform very similarly. These developed RFFA models can be used in south-eastern Australia to derive more accurate flood quantiles. The developed method can easily be adapted to other parts of Australia and other countries. The results of this study will assist in updating the Australian Rainfall Runoff (national guide)-recommended RFFA technique.

Design flood estimations at ungauged catchments are a challenging task in hydrology. Regional flood frequency analysis (RFFA) is widely used for this purpose. This paper develops artificial intelligence (AI)-based RFFA models (artificial neural networks (ANN) and support vector machine (SVM)) using data from 181 gauged catchments in South-East Australia. Based on an independent testing, it is found that the ANN method outperforms the SVM (the relative error values for the ANN model range 33–54% as compared to 37–64% for the SVM). The ANN and SVM models generate more accurate flood quantiles for smaller return periods; however, for higher return periods, both the methods present a higher estimation error. The results of this study will help to recommend new AI-based RFFA methods in Australia.

Flood is one of the most destructive natural disasters, causing significant economic damage and loss of lives. Numerous methods have been introduced to estimate design floods, which include linear and non-linear techniques. Since flood generation is a non-linear process, the use of linear techniques has inherent weaknesses. To overcome these, artificial intelligence (AI)-based non-linear regional flood frequency analysis (RFFA) techniques have been introduced over the last two decades. There are limited articles available in the literature discussing the relative merits/demerits of these AI-based RFFA techniques. To fill this knowledge gap, a scoping review on the AI-based RFFA techniques is presented. Based on the Scopus database, more than 1000 articles were initially selected, which were then screened manually to select the most relevant articles. The accuracy and efficiency of the selected RFFA techniques based on a set of evaluation statistics were compared. Furthermore, the relationships among countries and researchers focusing on AI-based RFFA techniques are illustrated. In terms of performance, artificial neural networks (ANN) are found to be the best performing techniques among all the selected AI-based RFFA techniques. It is also found that Australia, Canada, and Iran have published the highest number of articles in this research field, followed by Turkey, the United Arab Emirates (UAE), India, and China. Future research should be directed towards identification of the impacts of data quantity and quality, model uncertainty and climate change on the AI-based RFFA techniques.

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This study introduces a relatively simple technique for the manufacture of superhydrophobic coatings on polymeric surfaces. Plastics such as unplasticized poly(vinyl chloride) (UPVC) do not have a strong hydrophobic nature that is characterized by their low contact angles. Techniques of both increasing surface roughness and lowering surface energy are required to change their hydrophilicity to superhydrophobicity. In the present study, a coating of a low-surface-energy thermoplastic polyurethane (TPU) was spin-coated with chemically treated nanosilica to reduce the surface energy of UPVC. Nanosilica particles were embedded on the surface using a hot-press. Taguchi design was used to optimize multiple processing parameters. Samples spin-coated with 10 g L −1 nanosilica suspension in ethanol at a rate of 400 rpm for 5 s and then hot-pressed at 155 ∘ C under 2 atm (203 kPa) for 4 min had a contact angle of ca 157 ∘ and sliding angle of ca 6 ∘ , which are characteristic of superhydrophobic surfaces. Atomic force microscopy (AFM) and scanning electron microscopy (SEM) imaging showed that these superhydrophobic surfaces were highly rough with nanoscale features. Peel test and SEM analysis showed that silica nanoparticles embedded in the TPU coating were more stable than particles immobilized on UPVC sheet without TPU coating, proving that a layer of more flexible coating can improve the longevity of superhydrophobic surfaces manufactured using this facile method.