8 research outputs found

    Urban pluvial flooding prediction by machine learning approaches – a case study of Shenzhen city, China

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    Urban pluvial flooding is a threatening natural hazard in urban areas all over the world, especially in recent years given its increasing frequency of occurrence. In order to prevent flood occurrence and mitigate the subsequent aftermath, urban water managers aim to predict precipitation characteristics, including peak intensity, arrival time and duration, so that they can further warn inhabitants in risky areas and take emergency actions when forecasting a pluvial flood. Previous studies that dealt with the prediction of urban pluvial flooding are mainly based on hydrological or hydraulic models, requiring a large volume of data for simulation accuracy. These methods are computationally expensive. Using a rainfall threshold to predict flooding based on a data-driven approach can decrease the computational complexity to a great extent. In order to prepare cities for frequent pluvial flood events – especially in the future climate – this paper uses a rainfall threshold for classifying flood vs. non-flood events, based on machine learning (ML) approaches, applied to a case study of Shenzhen city in China. In doing so, ML models can determine several rainfall threshold lines projected in a plane spanned by two principal components, which provides a binary result (flood or no flood). Compared to the conventional critical rainfall curve, the proposed models, especially the subspace discriminant analysis, can classify flooding and non-flooding by different combinations of multiple-resolution rainfall intensities, greatly raising the accuracy to 96.5% and lowering the false alert rate to 25%. Compared to the conventional model, the critical indices of accuracy and true positive rate (TPR) were 5%-15% higher in ML models. Such models are applicable to other urban catchments as well. The results are expected to be used to assist early warning systems and provide rational information for contingency and emergency planning.Hydraulic Structures and Flood RiskWater Resource

    Prediction of River Pollution Under the Rainfall-Runoff Impact by Artificial Neural Network: A Case Study of Shiyan River, Shenzhen, China

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    Climate change and rapid urbanization have made it difficult to predict the risk of pollution in cities under different types of rainfall. In this study, a data-driven approach to quantify the effects of rainfall characteristics on river pollution was proposed and applied in a case study of Shiyan River, Shenzhen, China. The results indicate that the most important factor affecting river pollution is the dry period followed by average rainfall intensity, maximum rainfall in 10 min, total amount of rainfall, and initial runoff intensity. In addition, an artificial neural network model was developed to predict the event mean concentration (EMC) of COD in the river based on the correlations between rainfall characteristics and EMC. Compared to under light rain (< 10 mm/day), the predicted EMC was five times lower under heavy rain (25–49.9 mm/day) and two times lower under moderate rain (10–24.9 mm/day). By converting the EMC to chemical oxygen demand in the river, the pollution load under non-point-source runoff was estimated to be 497.6 t/year (with an accuracy of 95.98%) in Shiyan River under typical rainfall characteristics. The results of this study can be used to guide urban rainwater utilization and engineering design in Shenzhen. The findings also provide insights for predicting the risk of rainfall-runoff pollution and developing related policies in other cities.Hydraulic Structures and Flood Ris

    Is the tropical cyclone surge in Shanghai more sensitive to landfall location or intensity change?

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    It has been shown that the proportion of intense tropical cyclones (TCs) has been increasing together with a poleward migration of TC track. However, their relative importance to TC surge at landfall remains unknown. Here we examine the sensitivity of TC surge in Shanghai to landfall location and intensity with a new dynamical modelling framework. We find a surge sensitivity of 0.8 m (°N)−1 to landfall location, and 0.1 m (m s−1)−1 to wind speed in Shanghai during landfall. The landfall location and intensity are comparably important to surge variation. However, based on a plausible range of reported trends of TC poleward migration and intensity, the potential surge hazard due to poleward migration is estimated to be about three times larger than that by intensity change. The long-term surge risk in Shanghai is therefore substantially more sensitive to changes of TC track and landfall location than intensity. This may also be true elsewhere and in the future.Coastal EngineeringHydraulic Structures and Flood Ris

    Compound flood impact of water level and rainfall during tropical cyclone periods in a coastal city: the case of Shanghai

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    Compound flooding is generated when two or more flood drivers occur simultaneously or in close succession. Multiple drivers can amplify each other and lead to greater impacts than when they occur in isolation. A better understanding of the interdependence between flood drivers would facilitate a more accurate assessment of compound flood risk in coastal regions. This study employed the D-Flow Flexible Mesh model to simulate the historical peak coastal water level, consisting of the storm surge, astronomical tide, and relative sea level rise (RSLR), in Shanghai over the period 1961-2018. It then applies a copula-based methodology to calculate the joint probability of peak water level and rainfall during historical tropical cyclones (TCs) and to calculate the marginal contribution of each driver. The results indicate that the astronomical tide is the leading driver of peak water level, followed by the contribution of the storm surge. In the longer term, the RSLR has significantly amplified the peak water level. This study investigates the dependency of compound flood events in Shanghai on multiple drivers, which helps us to better understand compound floods and provides scientific references for flood risk management and for further studies. The framework developed in this study could be applied to other coastal cities that face the same constraint of unavailable water level records.Hydraulic Structures and Flood RiskCoastal Engineerin

    Analytical methods for sensing of health-hazardous arsenic from biotic and abiotic natural resources

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    Search for anomaly-mediated supersymmetry breaking with the ATLAS detector based on a disappearing-track signature in pp collisions at sqrt(s) = 7TeV$

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M. Giorgi;P. Giovannini;P. F. Giraud;D. Giugni;M. Giunta;P. Giusti;B. K. Gjelsten;L. K. Gladilin;C. Glasman;J. Glatzer;A. Glazov;K. W. Glitza;G. L. Glonti;J. Godfrey;J. Godlewski;M. Goebel;T. Göpfert;C. Goeringer;C. Gössling;T. Göttfert;S. Goldfarb;T. Golling;S. N. Golovnia;A. Gomes;L. S. Gomez Fajardo;R. Gonçalo;J. Goncalves Pinto Firmino Da Costa;L. Gonella;A. Gonidec;S. Gonzalez;S. González de la Hoz;G. Gonzalez Parra;M. L. Gonzalez Silva;S. Gonzalez-Sevilla;J. J. Goodson;L. Goossens;P. A. Gorbounov;H. A. Gordon;I. Gorelov;G. Gorfine;B. Gorini;E. Gorini;A. Gorišek;E. Gornicki;S. A. Gorokhov;V. N. Goryachev;B. Gosdzik;M. Gosselink;M. I. Gostkin;I. Gough Eschrich;M. Gouighri;D. Goujdami;M. P. Goulette;A. G. Goussiou;C. Goy;S. Gozpinar;I. Grabowska-Bold;P. Grafström;K-J. Grahn;F. Grancagnolo;S. Grancagnolo;V. Grassi;V. Gratchev;N. Grau;H. M. Gray;J. A. Gray;E. Graziani;O. G. Grebenyuk;T. Greenshaw;Z. D. Greenwood;K. Gregersen;I. M. Gregor;P. Grenier;J. Griffiths;N. Grigalashvili;A. A. Grillo;S. Grinstein;Y. V. Grishkevich;J.-F. Grivaz;M. Groh;E. Gross;J. Grosse-Knetter;J. Groth-Jensen;K. Grybel;V. J. Guarino;D. Guest;C. Guicheney;A. Guida;S. Guindon;H. Guler;J. Gunther;B. Guo;J. Guo;A. Gupta;Y. Gusakov;V. N. Gushchin;A. Gutierrez;P. Gutierrez;N. Guttman;O. Gutzwiller;C. Guyot;C. Gwenlan;C. B. Gwilliam;A. Haas;S. Haas;C. Haber;H. K. Hadavand;D. R. Hadley;P. Haefner;F. Hahn;S. Haider;Z. Hajduk;H. Hakobyan;D. Hall;J. Haller;K. Hamacher;P. Hamal;M. Hamer;A. Hamilton;S. Hamilton;H. Han;L. Han;K. Hanagaki;K. Hanawa;M. Hance;C. Handel;P. Hanke;J. R. Hansen;J. B. Hansen;J. D. Hansen;P. H. Hansen;P. Hansson;K. Hara;G. A. Hare;T. Harenberg;S. Harkusha;D. Harper;R. D. Harrington;O. M. Harris;K. Harrison;J. Hartert;F. Hartjes;T. Haruyama;A. Harvey;S. Hasegawa;Y. Hasegawa;S. Hassani;M. Hatch;D. Hauff;S. Haug;M. Hauschild;R. Hauser;M. Havranek;B. M. Hawes;C. M. Hawkes;R. J. Hawkings;D. Hawkins;T. Hayakawa;T. Hayashi;D. Hayden;H. S. Hayward;S. J. Haywood;E. Hazen;M. He;S. J. Head;V. Hedberg;L. Heelan;S. Heim;B. Heinemann;S. Heisterkamp;L. Helary;C. Heller;M. Heller;S. Hellman;D. Hellmich;C. Helsens;R. C. W. Henderson;M. Henke;A. Henrichs;A. M. Henriques Correia;S. Henrot-Versille;F. Henry-Couannier;C. Hensel;T. Henß;C. M. Hernandez;Y. Hernández Jiménez;R. Herrberg;A. D. Hershenhorn;G. Herten;R. Hertenberger;L. Hervas;N. P. Hessey;E. Higón-Rodriguez;D. Hill;J. C. Hill;N. Hill;K. H. Hiller;S. Hillert;S. J. Hillier;I. Hinchliffe;E. Hines;M. Hirose;F. Hirsch;D. Hirschbuehl;J. Hobbs;N. Hod;M. C. Hodgkinson;P. Hodgson;A. Hoecker;M. R. Hoeferkamp;J. Hoffman;D. Hoffmann;M. Hohlfeld;M. Holder;S. O. Holmgren;T. Holy;J. L. Holzbauer;Y. Homma;T. M. Hong;L. Hooft van Huysduynen;T. Horazdovsky;C. Horn;S. Horner;J-Y. Hostachy;S. Hou;M. A. Houlden;A. Hoummada;J. Howarth;D. F. Howell;I. Hristova;J. Hrivnac;I. Hruska;T. Hryn’ova;P. J. Hsu;S.-C. Hsu;G. S. Huang;Z. Hubacek;F. Hubaut;F. Huegging;T. B. Huffman;E. W. Hughes;G. Hughes;R. E. Hughes-Jones;M. Huhtinen;P. Hurst;M. Hurwitz;U. Husemann;N. Huseynov;J. Huston;J. Huth;G. Iacobucci;G. Iakovidis;M. Ibbotson;I. Ibragimov;R. Ichimiya;L. Iconomidou-Fayard;J. Idarraga;P. Iengo;O. Igonkina;Y. Ikegami;M. Ikeno;Y. Ilchenko;D. Iliadis;N. Ilic;D. Imbault;M. Imori;T. Ince;J. Inigo-Golfin;P. Ioannou;M. Iodice;A. Irles Quiles;C. Isaksson;A. Ishikawa;M. Ishino;R. Ishmukhametov;C. Issever;S. Istin;A. V. Ivashin;W. Iwanski;H. Iwasaki;J. M. Izen;V. Izzo;B. Jackson;J. N. Jackson;P. Jackson;M. R. Jaekel;V. Jain;K. Jakobs;S. Jakobsen;J. Jakubek;D. K. Jana;E. Jankowski;E. Jansen;H. Jansen;A. Jantsch;M. Janus;G. Jarlskog;L. Jeanty;K. Jelen;I. Jen-La Plante;P. Jenni;A. Jeremie;P. Jež;S. Jézéquel;M. K. Jha;H. Ji;W. Ji;J. Jia;Y. Jiang;M. Jimenez Belenguer;G. Jin;S. Jin;O. Jinnouchi;M. D. Joergensen;D. Joffe;L. G. Johansen;M. Johansen;K. E. Johansson;P. Johansson;S. Johnert;K. A. Johns;K. Jon-And;G. Jones;R. W. L. Jones;T. W. Jones;T. J. Jones;O. Jonsson;C. Joram;P. M. Jorge;J. Joseph;T. Jovin;X. Ju;C. A. Jung;V. Juranek;P. Jussel;A. Juste Rozas;V. V. Kabachenko;S. Kabana;M. Kaci;A. Kaczmarska;P. Kadlecik;M. Kado;H. Kagan;M. Kagan;S. Kaiser;E. Kajomovitz;S. Kalinin;L. V. Kalinovskaya;S. Kama;N. Kanaya;M. Kaneda;T. Kanno;V. A. Kantserov;J. Kanzaki;B. Kaplan;A. Kapliy;J. Kaplon;D. Kar;M. Karagounis;M. Karagoz;M. Karnevskiy;K. Karr;V. Kartvelishvili;A. N. Karyukhin;L. Kashif;G. Kasieczka;R. D. Kass;A. Kastanas;M. Kataoka;Y. Kataoka;E. Katsoufis;J. Katzy;V. Kaushik;K. Kawagoe;T. Kawamoto;G. Kawamura;M. S. Kayl;V. A. Kazanin;M. Y. Kazarinov;J. R. Keates;R. Keeler;R. Kehoe;M. Keil;G. D. Kekelidze;J. Kennedy;C. J. Kenney;M. Kenyon;O. Kepka;N. Kerschen;B. P. Kerševan;S. Kersten;K. Kessoku;J. Keung;F. Khalil-zada;H. Khandanyan;A. Khanov;D. Kharchenko;A. Khodinov;A. G. Kholodenko;A. Khomich;T. J. Khoo;G. Khoriauli;A. Khoroshilov;N. Khovanskiy;V. Khovanskiy;E. Khramov;J. Khubua;H. Kim;M. S. Kim;P. C. Kim;S. H. Kim;N. Kimura;O. Kind;B. T. King;M. King;R. S. B. King;J. Kirk;L. E. Kirsch;A. E. Kiryunin;T. Kishimoto;D. Kisielewska;T. Kittelmann;A. M. Kiver;E. Kladiva;J. Klaiber-Lodewigs;M. Klein;U. Klein;K. Kleinknecht;M. Klemetti;A. Klier;A. Klimentov;R. Klingenberg;E. B. Klinkby;T. Klioutchnikova;P. F. Klok;S. Klous;E.-E. Kluge;T. Kluge;P. Kluit;S. Kluth;N. S. Knecht;E. Kneringer;J. Knobloch;E. B. F. G. Knoops;A. Knue;B. R. Ko;T. Kobayashi;M. Kobel;M. Kocian;P. Kodys;K. Köneke;A. C. König;S. Koenig;L. Köpke;F. Koetsveld;P. Koevesarki;T. Koffas;E. Koffeman;F. Kohn;Z. Kohout;T. Kohriki;T. Koi;T. Kokott;G. M. Kolachev;H. Kolanoski;V. Kolesnikov;I. Koletsou;J. Koll;D. Kollar;M. Kollefrath;S. D. Kolya;A. A. Komar;Y. Komori;T. Kondo;T. Kono;A. I. Kononov;R. Konoplich;N. Konstantinidis;A. Kootz;S. Koperny;S. V. Kopikov;K. Korcyl;K. Kordas;V. Koreshev;A. Korn;A. Korol;I. Korolkov;E. V. Korolkova;V. A. Korotkov;O. Kortner;S. Kortner;V. V. Kostyukhin;M. J. Kotamäki;S. Kotov;V. M. Kotov;A. Kotwal;C. Kourkoumelis;V. Kouskoura;A. Koutsman;R. Kowalewski;T. Z. Kowalski;W. Kozanecki;A. S. Kozhin;V. Kral;V. A. Kramarenko;G. Kramberger;M. W. Krasny;A. Krasznahorkay;J. Kraus;J. K. Kraus;A. Kreisel;F. Krejci;J. Kretzschmar;N. Krieger;P. Krieger;K. Kroeninger;H. Kroha;J. Kroll;J. Kroseberg;J. Krstic;U. Kruchonak;H. Krüger;T. Kruker;N. Krumnack;Z. V. Krumshteyn;A. Kruth;T. Kubota;S. Kuehn;A. Kugel;T. Kuhl;D. Kuhn;V. Kukhtin;Y. Kulchitsky;S. Kuleshov;C. Kummer;M. Kuna;N. Kundu;J. Kunkle;A. Kupco;H. Kurashige;M. Kurata;Y. A. Kurochkin;V. Kus;M. Kuze;J. Kvita;R. Kwee;A. Rosa;L. Rotonda;L. Labarga;J. Labbe;S. Lablak;C. Lacasta;F. Lacava;H. Lacker;D. Lacour;V. R. Lacuesta;E. Ladygin;R. Lafaye;B. Laforge;T. Lagouri;S. Lai;E. Laisne;M. Lamanna;C. L. Lampen;W. Lampl;E. Lancon;U. Landgraf;M. P. J. Landon;H. Landsman;J. L. Lane;C. Lange;A. J. Lankford;F. Lanni;K. Lantzsch;S. Laplace;C. Lapoire;J. F. Laporte;T. Lari;A. V. Larionov;A. Larner;C. Lasseur;M. Lassnig;P. Laurelli;W. Lavrijsen;P. Laycock;A. B. Lazarev;O. Dortz;E. Guirriec;C. Maner;E. Menedeu;C. Lebel;T. LeCompte;F. Ledroit-Guillon;H. Lee;J. S. H. Lee;S. C. Lee;L. Lee;M. Lefebvre;M. Legendre;A. Leger;B. C. LeGeyt;F. Legger;C. Leggett;M. Lehmacher;G. Lehmann Miotto;X. Lei;M. A. L. Leite;R. Leitner;D. Lellouch;M. Leltchouk;B. Lemmer;V. Lendermann;K. J. C. Leney;T. Lenz;G. Lenzen;B. Lenzi;K. Leonhardt;S. Leontsinis;C. Leroy;J-R. Lessard;J. Lesser;C. G. Lester;A. Leung Fook Cheong;J. Levêque;D. Levin;L. J. Levinson;M. S. Levitski;A. Lewis;G. H. Lewis;A. M. Leyko;M. Leyton;B. Li;H. Li;S. Li;X. Li;Z. Liang;H. Liao;B. Liberti;P. Lichard;M. Lichtnecker;K. Lie;W. Liebig;R. Lifshitz;C. Limbach;A. Limosani;M. Limper;S. C. Lin;F. Linde;J. T. Linnemann;E. Lipeles;L. Lipinsky;A. Lipniacka;T. M. Liss;D. Lissauer;A. Lister;A. M. Litke;C. Liu;D. Liu;H. Liu;J. B. Liu;M. Liu;S. Liu;Y. Liu;M. Livan;S. S. A. Livermore;A. Lleres;J. Llorente Merino;S. L. Lloyd;E. Lobodzinska;P. Loch;W. S. Lockman;T. Loddenkoetter;F. K. Loebinger;A. Loginov;C. W. Loh;T. Lohse;K. Lohwasser;M. Lokajicek;J. Loken;V. P. Lombardo;R. E. Long;L. Lopes;D. Lopez Mateos;J. Lorenz;M. Losada;P. Loscutoff;F. Lo Sterzo;M. J. Losty;X. Lou;A. Lounis;K. F. Loureiro;J. Love;P. A. Love;A. J. Lowe;F. Lu;H. J. Lubatti;C. Luci;A. Lucotte;A. Ludwig;D. Ludwig;I. Ludwig;J. Ludwig;F. Luehring;G. Luijckx;D. Lumb;L. Luminari;E. Lund;B. Lund-Jensen;B. Lundberg;J. Lundberg;J. Lundquist;M. Lungwitz;G. Lutz;D. Lynn;J. Lys;E. Lytken;H. Ma;L. L. Ma;J. A. Macana Goia;G. Maccarrone;A. Macchiolo;B. Maček;J. Machado Miguens;R. Mackeprang;R. J. Madaras;W. F. Mader;R. Maenner;T. Maeno;P. Mättig;S. Mättig;L. Magnoni;E. Magradze;Y. Mahalalel;K. Mahboubi;G. Mahout;C. Maiani;C. Maidantchik;A. Maio;S. Majewski;Y. Makida;N. Makovec;P. Mal;Pa. Malecki;P. Malecki;V. P. Maleev;F. Malek;U. Mallik;D. Malon;C. Malone;S. Maltezos;V. Malyshev;S. Malyukov;R. Mameghani;J. Mamuzic;A. Manabe;L. Mandelli;I. Mandić;R. Mandrysch;J. Maneira;P. S. Mangeard;I. D. Manjavidze;A. Mann;P. M. Manning;A. Manousakis-Katsikakis;B. Mansoulie;A. Manz;A. Mapelli;L. Mapelli;L. March;J. F. Marchand;F. Marchese;G. Marchiori;M. Marcisovsky;A. Marin;C. P. Marino;F. Marroquim;R. Marshall;Z. Marshall;F. K. Martens;S. Marti-Garcia;A. J. Martin;B. Martin;B. Martin;F. F. Martin;J. P. Martin;Ph. Martin;T. A. Martin;V. J. Martin;B. Ma

    Measurement of τ

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