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首页> 外文会议>2017 IEEE 3rd International Conference on Engineering Technologies and Social Sciences >Prior investigation for flash floods and hurricanes, concise capsulization of hydrological technologies and instrumentation: A survey
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Prior investigation for flash floods and hurricanes, concise capsulization of hydrological technologies and instrumentation: A survey

机译：事先对山洪和飓风进行调查，对水文技术和仪器进行简明扼要的描述：一项调查

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摘要

Intense and severe torrents, tornadoes and thunderstorm causes numerous casualties in fraction of second and extreme devastation of infrastructure in many countries. Flash floods are considered one of the most hazardous natural disasters. Several approaches have been made for an authentic and robust early warning system to forecast the flash floods vigorously. An intelligent and vibrant model for the recognition of floods includes the estimation of water level, Global Positioning System-Precipitable Water Vapor (GPS PWV), precipitation velocity, wind speed, direction, upstream levels of river, soil moisture, oceanic bottom pressure and color of the water with accurate and reliable cognition algorithms. UGS (unattended ground sensors) and langrangian micro transducers are deployed on the ground and spread on the sea surface respectively to investigate the hydrological and meteorological differences on real time basis. By the utilization of fuzzy logic, Kalman filtering, Adaptive neuro fuzzy interference system (ANFIS), Particle Swarm Optimization (PSO) and Neural network autoregressive model with exogenous input (NNARX) based structure. Reduction of complexities in data collection, high false alarm rates, communication issues, low WSN battery backup and all related hindrances have been the focal point of this research paper.
机译：在许多国家，强烈而猛烈的洪流，龙卷风和雷暴导致大量人员伤亡，而基础设施遭受的破坏却仅次于其次。山洪被认为是最危险的自然灾害之一。对于可靠可靠的预警系统，已经采取了几种方法来大力预测山洪暴发。一个智能且充满活力的洪水识别模型包括水位估算，全球定位系统-可降水量水汽（GPS PWV），降水速度，风速，方向，河流上游水位，土壤湿度，海洋底压和颜色准确可靠的认知算法对水进行分析。 UGS（无人值守地面传感器）和郎朗日式微传感器分别部署在地面上并分布在海面上，以实时调查水文和气象差异。利用模糊逻辑，卡尔曼滤波，自适应神经模糊干扰系统（ANFIS），粒子群优化（PSO）和基于外来输入的神经网络自回归模型（NNARX）。减少数据收集的复杂性，提高误报率，减少通信问题，减少WSN电池备用以及所有相关障碍已成为本研究的重点。

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