Assessing Surface Water Flood Risks in Urban Areas Using Machine Learning

Urban flooding is a devastating natural hazard for cities around the world. Flood risk mapping is a key tool in flood management. However, it is computationally expensive to produce flood risk maps using hydrodynamic models. To this end, this paper investigates the use of machine learning for the assessment of surface water flood risks in urban areas. The factors that are considered in machine learning models include coordinates, elevation, slope gradient, imperviousness, land use, land cover, soil type, substrate, distance to river, distance to road, and normalized difference vegetation index. The machine learning models are tested using the case study of Exeter, UK. The performance of machine learning algorithms, including naïve Bayes, perceptron, artificial neural networks (ANNs), and convolutional neural networks (CNNs), is compared based on a spectrum of indicators, e.g., accuracy, F-beta score, and receiver operating characteristic curve. The results obtained from the case study show that the flood risk maps can be accurately generated by the machine learning models. The performance of models on the 30-year flood event is better than 100-year and 1000-year flood events. The CNNs and ANNs outperform the other machine learning algorithms tested. This study shows that machine learning can help provide rapid flood mapping, and contribute to urban flood risk assessment and management

Underwater Targets Radiated Noise Classification Based on Enhanced Images and Convolutional Neural Networks

As the economy and society continue to develop, the types of underwater targets are becoming increasingly diverse, accompanied by a corresponding increase in environmental noise. This noise presents a significant challenge for the recognition of underwater target radiation noise, as it can result in the loss of effective information during the noise processing stage. This, in turn, reduces the accuracy of underwater target radiation noise recognition. This paper proposes an underwater target radiation noise recognition method based on the enhanced image method. The method transforms the original underwater target radiation noise signal into an enhanced image, builds a convolutional neural network with the enhanced image as an input, and uses the convolutional neural network’s ability to classify images to recognise and classify underwater target radiation noise. The experimental results demonstrate that the training time of the method described in this paper is longer than that of the traditional machine learning method, yet the recognition and classification accuracy is significantly higher

Diversity and Evolution of the Avirulence Gene <i>AvrPi54</i> in Yunnan Rice Fields

Variance or complete loss of the avirulence gene (Avr) enables the pathogen to escape resistance protein (R) recognition. The field resistance effectiveness of the R gene is determined by its corresponding Avr gene in field isolates. To effectively deploy the rice blast R gene Pi54, the distribution, variation and evolution of the corresponding Avr gene, AvrPi54, were determined through PCR amplification, pathogenicity assay, gene sequences and evolutionary analysis. Among 451 Pyricularia isolates from rice and non-rice hosts, including Oryza rufipogon, Digitaria sanguinalis, Eleusine coracana, E. indica and Musa sp. in Yunnan province, the PCR amplification result showed that AvrPi54 alleles existed among 218 (48.3%) isolates including rice isolates, O. rufipogon isolates and E. coracana isolates. Pathogenicity assay showed that 336 (74.5%) isolates were avirulent to Tetep (holding Pi54). Five AvrPi54 haplotypes were identified among 142 isolates through the gene sequence. These haplotypes were determined to be avirulent to Pi54 through pathogenicity assay. Four novel haplotypes (H2 to H5) of the AvrPi54 gene would provide new target sites for rice blast control. Haplotype diversity analysis indicated that there existed a lower genetic diversity of AvrPi54 for P. oryzae populations (five haplotypes, Hd = 0.127, π = 2.9 × 10−4) in this study. Neutrality tests showed that AvrPi54’s genetic variation was affected by purified selection. Haplotype network and phylogeny analysis showed that H1 was an ancestral haplotype and was widely distributed in rice isolates and O. rufipogon isolates, while H5 diverged early and evolved independently. These results indicate that the gene evolves slowly and stably and is a comparatively conserved Avr gene