Development of the hydrological rainfall-runoff model based on artificial neural network in small catchments

U nekim je situacijama štetne pojave, koje su uzrokovane djelovanjem vode, moguće umanjiti ili spriječiti posljedice istih uz pomoć sustava za rano uzbunjivanje pravovremenom obznanom obavijesti o mogućnosti nastanka štetne pojave. Motivacija za izradu ove disertacije temelji se na istraživanju mogućnosti predviđanja štetnih pojava uzrokovanih vodom na malim slivovima u cilju implementacije sustava za rano uzbunjivanje. Istraživanja unutar rada obuhvaćaju uspostavu kontinuiranog mjerenja meteoroloških i hidroloških podataka na istražnom području sliva Slani potok (Vinodolska dolina) koji je povijesno prepoznato hazardno područje, primjenu umjetnih neuronskih mreža pri razvoju hidrološkog modela predviđanja otjecanja, određivanje načina validacije i evalvacije modela te razvoj metodologije implementacije hidrološkog modela predviđanja otjecanja na malim slivovima. Shodno provedenom istraživanju, a u cilju dokazivanja postavljenih hipoteza razvijen je hidrološki model predviđanja otjecanja s malih slivova temeljen na umjetnoj neuronskoj mreži. Prikupljeni podaci korišteni su za treniranje, validaciju te evalvaciju mogućnosti predviđanja hidrološkog modela otjecanja. Model je potom validiran i evalviran vizualnim i numeričkim mjerama kvalitete prilikom čega su utvrđene dostatne mogućnosti predviđanja modela za potrebe implementacije sustava ranog uzbunjivanja. Temeljem razvijenog modela utvrđena je detaljna metodologija za implementaciju hidrološkog modela otjecanja na malim slivovima temeljenog na umjetnoj neuronskoj mreži.Occasionally, consequences caused by water induced events can somewhat be reduced or even prevented with the help of an early warning system whose aim is timely notification of local population on potentially upcoming hazardous event. The motivation for this thesis arises from the need to explore the possibilities to foresee such water caused events on small catchments with an aim to mitigate its consequences by implementing an early warning system. Research and analysis shown in this theses encompasses the establishment of continuous meteorological and hydrological data monitoring, on research area Slani Potok (Vinodol Valley) historically known as potentially hazardous area, the application of the artificial neural network as a means for the development of hydrological rainfall-runoff model, defining the methods for model validation and evaluation, as well as the development of the methodology for the hydrological rainfall-runoff model implementation on small catchments. Upon on this research a hydrological rainfall-runoff model for small catchments was developed based on artificial neural network. Gathered data was used for training, validation and evaluation of model`s accuracy and precision in rainfall-runoff prediction. The model was validated and evaluated using visual and numerical quality measures according to which needed accuracy in model prediction was determined for the implementation of early warning system. Based on this model a detailed methodology for the implementation of rainfall-runoff model on small catchments developed on artificial neural network was established

Methodology for Developing Hydrological Models Based on an Artificial Neural Network to Establish an Early Warning System in Small Catchments

In some situations, there is no possibility of hazard mitigation, especially if the hazard is induced by water. Thus, it is important to prevent consequences via an early warning system (EWS) to announce the possible occurrence of a hazard. The aim and objective of this paper are to investigate the possibility of implementing an EWS in a small-scale catchment and to develop a methodology for developing a hydrological prediction model based on an artificial neural network (ANN) as an essential part of the EWS. The methodology is implemented in the case study of the Slani Potok catchment, which is historically recognized as a hazard-prone area, by establishing continuous monitoring of meteorological and hydrological parameters to collect data for the training, validation, and evaluation of the prediction capabilities of the ANN model. The model is validated and evaluated by visual and common calculation approaches and a new evaluation for the assessment. This new evaluation is proposed based on the separation of the observed data into classes based on the mean data value and the percentages of classes above or below the mean data value as well as on the performance of the mean absolute error

Development of the hydrological rainfall-runoff model based on artificial neural network in small catchments

U nekim je situacijama štetne pojave, koje su uzrokovane djelovanjem vode, moguće umanjiti ili spriječiti posljedice istih uz pomoć sustava za rano uzbunjivanje pravovremenom obznanom obavijesti o mogućnosti nastanka štetne pojave. Motivacija za izradu ove disertacije temelji se na istraživanju mogućnosti predviđanja štetnih pojava uzrokovanih vodom na malim slivovima u cilju implementacije sustava za rano uzbunjivanje. Istraživanja unutar rada obuhvaćaju uspostavu kontinuiranog mjerenja meteoroloških i hidroloških podataka na istražnom području sliva Slani potok (Vinodolska dolina) koji je povijesno prepoznato hazardno područje, primjenu umjetnih neuronskih mreža pri razvoju hidrološkog modela predviđanja otjecanja, određivanje načina validacije i evalvacije modela te razvoj metodologije implementacije hidrološkog modela predviđanja otjecanja na malim slivovima. Shodno provedenom istraživanju, a u cilju dokazivanja postavljenih hipoteza razvijen je hidrološki model predviđanja otjecanja s malih slivova temeljen na umjetnoj neuronskoj mreži. Prikupljeni podaci korišteni su za treniranje, validaciju te evalvaciju mogućnosti predviđanja hidrološkog modela otjecanja. Model je potom validiran i evalviran vizualnim i numeričkim mjerama kvalitete prilikom čega su utvrđene dostatne mogućnosti predviđanja modela za potrebe implementacije sustava ranog uzbunjivanja. Temeljem razvijenog modela utvrđena je detaljna metodologija za implementaciju hidrološkog modela otjecanja na malim slivovima temeljenog na umjetnoj neuronskoj mreži.Occasionally, consequences caused by water induced events can somewhat be reduced or even prevented with the help of an early warning system whose aim is timely notification of local population on potentially upcoming hazardous event. The motivation for this thesis arises from the need to explore the possibilities to foresee such water caused events on small catchments with an aim to mitigate its consequences by implementing an early warning system. Research and analysis shown in this theses encompasses the establishment of continuous meteorological and hydrological data monitoring, on research area Slani Potok (Vinodol Valley) historically known as potentially hazardous area, the application of the artificial neural network as a means for the development of hydrological rainfall-runoff model, defining the methods for model validation and evaluation, as well as the development of the methodology for the hydrological rainfall-runoff model implementation on small catchments. Upon on this research a hydrological rainfall-runoff model for small catchments was developed based on artificial neural network. Gathered data was used for training, validation and evaluation of model`s accuracy and precision in rainfall-runoff prediction. The model was validated and evaluated using visual and numerical quality measures according to which needed accuracy in model prediction was determined for the implementation of early warning system. Based on this model a detailed methodology for the implementation of rainfall-runoff model on small catchments developed on artificial neural network was established

Prediction models for manganese, iron and ammonium in raw water for a drinking water treatment plant Butoniga (croatia)

Drinking water treatment plant Butoniga is one of the main water supply facilities for potable water in Istria (Croatia). Water for treatment process is captured from the Butoniga reservoir which is a small and relatively shallow reservoir. As such, the reservoir is very sensitive to eutrophication and degradation processes caused by climate change and human activities in the watershed. In summer months during tourist season, when at highest water demand and lowest water level at the reservoir, the water temperature is the most critical parameter during treatment process. To capture colder water, raw water for treatment is taken from the lowest water intake, i.e. from the deepest layer in the Butoniga reservoir. This layer has another problem, namely increased concentrations of manganese, iron and ammonium under lower pH values. This study provides prediction models for manganese, iron and ammonium for seven days in advance, which are some of the most critical parameters during summer months and have significant influence on treatment process of raw water. For modelling purposes, machine learning software Weka was used to build models in form of model trees. Obtained prediction models for manganese, iron and ammonium have high accuracy compared to the measured data with a good prediction of the peak values. Therefore, obtained models can help in optimization of the treatment processes at the treatment plant, which are depending on the quality of raw water in Butoniga reservoir

Analysis of the Runoff Coefficient Changes During the Year on Slani Potok Catchment Area

Koeficijent otjecanja sa sliva jest odnos efektivne (neto) oborine i oborine koja padne na sliv (bruto oborina) i veoma je značajna varijabla u analizama procesa otjecanja oborine s određenog sliva i vodne bilance. Koeficijent otjecanja računa se uobičajeno na godišnjoj i mjesečnoj razini, a u ovom je radu prikazana njegova promjena tijekom pojedinih kišnih događaja u jednoj godini i analizirani su faktori koji na nju značajno utječu. Na temelju prikupljenih meteoroloških i hidroloških podataka značajnih oborinskih epizoda za 2014. godinu prikazani su rezultati analize godišnje promjene koeficijenta otjecanja sa sliva Slanog potoka.The runoff coefficient represents the ratio of the effective (net) precipitation to the precipitation that falls on the basin (gross rainfall) and it is a very important variable in the analysis of the rainfall and runoff process and the water balance. The runoff coefficient is usually calculated on an annual and monthly basis. The aim of this paper is to show the change during the year and the factors that affect it. The results of the runoff coefficient change analysis during the year on the case study of Slani potok catchment have been presented. The analysis was conducted on the collected meteorological and hydrological data of significant rainfall episodes during 2014

SCS Method Application in Construction of the Runoff Hydrograph

Primjena SCS metode je u Hrvatskoj temeljena na priručnicima inozemnih izvora te već pripremljenih programskih paketa. U radu je prikazana kratka povijest razvoja SCS metode i njezine karakteristike te postupak izračuna protoka i konstruiranja hidrograma otjecanja primjenom iste, a sve u cilju poboljšanja razumijevanja. Detaljno su opisani koraci izračuna efektivne oborine korištenjem SCS metode, izračun maksimalnih protoka Ven Te Chow metodom te primjena Goudrich-ovog izraza za modifikaciju i definiranje svih točaka hidrograma. Objašnjenje primjene SCS metode potkrijepljeno je primjerom izračuna na slivu Slanog potoka.Application of the SCS method in the Republic of Croatia is primarily based on manuals from foreign sources and already prepared software packages. This paper will present a brief history of the SCS method development and its characteristics, as well as the process of calculation of the flow and construction of the runoff hydrographs. The application of the SCS method for the runoff precipitation assessment in the maximum runoff calculation by usage of Ven Te Chow method and Goudrich expression for modifying and defining all hydrograph points are described in detail. The example of a calculation on the Slani potok catchment is presented

Pregnancy and delivery outcome in young and older primigravidae

Cilj istraživanja: Utvrditi i usporediti ishod trudnoće i poroda na početku i pri kraju reproduktivne dobi. Materijal i metode: U istraživanje je uključeno 59 prvorotkinja mlađih od 18 godina i 233 prvorotkinje starije od 35 godina, koje su rodile u našoj ustanovi tijekom dvogodišnjeg razdoblja (od 2006. do 2007. god.). Podaci su prikupljeni retrospektivno, pretraživanjem medicinske dokumentacije i rađaoničkog protokola hospitaliziranih rodilja. Rezultati: U starih prvorotkinja značajno je povećan broj medicinskih zahvata u trudnoći kao i učestalost komplikacija u trudnoći (36,48% vs. 16,94%, p<0,05). U starih prvorotkinja značajno je češća i učestalost operativnog dovršenja poroda carskim rezom (23,60% vs. 8,47%, p<0,05) iako nije uočena značajna razlika u nepravilnostima rađanja. Mlade prvorotkinje znatno češće rađaju uz epiduralnu analgeziju (32,20% vs. 25,32%, p<0,01). Ne postoji značajna razlika u trajanju gestacije, u srednjoj vrijednosti rodne mase, niti razlika u Apgar score novorođenčadi. Zaključak: Mlade i stare prvorotkinje su rizične skupine rodilja koje zahtijevaju posebnu opstetričku skrb i planiranje načina rađanja. Negativan učinak životne dobi na ishod trudnoće jače je izražen u starijih prvorotkinjaAim: Tto determine and compare the frequency of the risk of pregnancy and delivery outcome at the beginning and ending of reproductive age. Methods: The study included 59 nulliparous adolescents younger than 18 years and 233 nulliparous women 35 years and older in the period of two years (2006-2007). Birth records and patient files were retrospectively analised and compared. Results: In the primiparae of 35 years or more, medical interventions during pregnancy were higher, and the risk of chronic diseases which complicated their pregnancies increased (36,48% vs. 16,94, p<0,05). The incidence of cesarean section was statistically higher in pregnancies above 35 years (32,20% vs. 25,32 % p<0,01). Birth weight, APGAR scores and, incidence of premature birth, were not significantly different between groups. Conclusion: Both adolescents and women of advanced reproductive age comprise distinct groups of obstetrics patients. Each has special needs and is susceptible to different obstetric risks. Nulliparous women of 35 years and older have higher risk of negative effect of age on their pregnancies