The key to leak detection and location in water supply pipelines is signal denoising and feature extraction. First, in this paper, an improved spline-local mean decomposition (ISLMD) is proposed to eliminate noise interference. Based on the ISLMD decomposition of a signal, the cross-correlation function between the reference signal and the product functions component can be obtained. And then the PF component containing the leak information can be extracted reasonably. Compared with improved local mean decomposition, the ISLMD has higher accuracy in leak location. Second, an image recognition method using a convolutional neural network for leak detection is proposed, which can better address the problem that the features of different leak apertures or locations are highly similar to each other. The images from the conversion of the reconstructed signals are used as the input of the AlexNet model, which is capable of adaptive extraction of leak signal features. The trained AlexNet model can effectively detect different leak apertures. Finally, the signal time-delay between the upstream and downstream pressure transmitters caused by the leak and propagation of negative pressure wave is determined according to generalized crosscorrelation analysis, and thereby, the leak location is obtained. The experimental results show that the proposed method is effective for leak detection and location. INDEX TERMS Local mean decomposition, convolutional neural network, generalized cross-correlation, leak detection and location, fault detection.
Agricultural production is substantially affected by the variations in global weather patterns, particularly by the El Niño–Southern Oscillation (ENSO). Thus, incorporating the forecast of imminent ENSO phases can enhance the effectiveness of crop insurance and mitigate the adverse impacts of weather on agriculture. Given the probabilistic nature of the ENSO phase forecast, we employ a Bayesian framework to estimate the value of ENSO information on various aspects of crop insurance. Our results indicate potential benefits of ENSO forecast to insurance rate setting and policy selection. At the same time, we caution against overoptimism in this assessment as economic benefits may diminish as the accuracy of ENSO forecast decreases. Simulations and numerical experiments demonstrate the practical usefulness of the proposed method for various stakeholders of the US crop insurance industry. Implications to various crop insurance policies are also discussed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.