scholarly journals Predictive Performance Analysis of PDF – IDF Model Types Using Rainfall Observations from Fourteen Gauged Stations

2021 ◽  
pp. 125-143
Author(s):  
Ify L. Nwaogazie ◽  
M. G. Sam ◽  
A. O. David
Keyword(s):  
Return Period ◽  
Flood Control ◽  
Goodness Of Fit ◽  
Rainfall Intensity ◽  
Model Development ◽  
Predictive Performance ◽  
Specific Model ◽  
Data Set ◽  
Pearson Type ◽  
Significant Difference

The design of structures for flood mitigation depends on the adequate estimation of rainfall intensity over a given catchment which is achieved by the rainfall intensity duration frequency modelling. In this study, an extensive comparative analyses were carried out on the predictive performance of three PDF – IDF model types, namely: Gumbel Extreme Value Type 1 (GEVT – 1), Log-Pearson Type 3 (LPT – 3) and Normal Distribution (ND) in 14 selected cities in Southern Nigeria. This is to rank the order of best performance. The principle of general model development was adopted in which rainfall intensities at different durations and specified return periods were used as input data set. This is not same as return period specific model that involves rainfall intensities for various durations and a given return period. The predicted rainfall intensity values with the PDF – IDF model types indicate high goodness of fit (R2) and Mean Squared Errors (MSE) ranging from: (a) R2 = 0.875 – 0.992; MSE = 33.17 – 224.6 for GEVT – 1; (b) R2 = 0.849 – 0.990; MSE = 65.34 – 405.5 for LPT – 3 and (c) R2 = 0.839 – 0.992; MSE = 29.23 – 200.2 for ND. The comparative analysis of all the 42 general models (14 locations versus 3 model types) considered showed that the order of best performance is LPT – 3 1st, GEVT - 1 2nd and ND 3rd for each return period (10, 50 and 100 years). The Kruskal Wallis test of significance indicates that no significant difference exists in the predictive performance of the three General models across the board. This may be due to the fact that the fourteen locations of the study area are bordering with the Atlantic Ocean and seems to have similar climatology. These developed General models are recommended for the computation of intensities in the fourteen locations for the design of flood control structures; and the order of preference should be LPT – 3 > GEVT – 1 > ND.

Sewer System for Improving Flood Control in Tokyo: A Step towards a Return Period of 70 Years

1994 ◽  
Vol 29 (1-2) ◽  
pp. 303-310 ◽  
Author(s):  
Kazuyuki Higuchi ◽  
Masahiro Maeda ◽  
Yasuyuki Shintani
Keyword(s):  
Return Period ◽  
Flood Control ◽  
Rainfall Intensity ◽  
Runoff Coefficient ◽  
Large Drop ◽  
Construction Costs ◽  
Sewer System ◽  
Construction Period ◽  
Metropolitan Government ◽  
Under Construction

The Tokyo Metropolitan Government has planned future flood control for a rainfall intensity of 100 mm/hr, which corresponds to a return period of 70 years, and a runoff coefficient of 0.8. Considering that the realization of this plan requires a long construction period and high construction costs, the decision was made to proceed by stages. In the first stage, the improvement of the facilities will be based on a rainfall intensity of 75 mm/hr (presently 50 mm/hr), corresponding to a return period of 17 years, and a runoff coefficient of 0.8. In the next stage the facilities will be improved to accommodate a rainfall intensity of 100 mm/hr. In the Nakano and Suginami regions, which suffer frequently from flooding, the plan of improvement based on a rainfall intensity of 75 mm/hr is being implemented before other areas. This facility will be used as a storage sewer for the time being. The Wada-Yayoi Trunk Sewer, as a project of this plan, will have a diameter of 8 m and a 50 m earth cover. This trunk sewer will be constructed considering several constraints. To resolve these problems, hydraulic experiments as well as an inventory study have been carried out. A large drop shaft for the trunk sewer is under construction.

scholarly journals Keep it simple - A case study of model development in the context of the Dynamic Stocks and Flows (DSF) task

2010 ◽  
Vol 2 (2) ◽  
pp. 38-51 ◽  
Author(s):  
Marc Halbrügge
Keyword(s):  
Goodness Of Fit ◽  
Model Development ◽  
Cognitive Model ◽  
Training Data ◽  
Sequence Matching ◽  
Data Set ◽  
Depth Analysis ◽  
Stocks And Flows ◽  
Matching Techniques

Keep it simple - A case study of model development in the context of the Dynamic Stocks and Flows (DSF) taskThis paper describes the creation of a cognitive model submitted to the ‘Dynamic Stocks and Flows’ (DSF) modeling challenge. This challenge aims at comparing computational cognitive models for human behavior during an open ended control task. Participants in the modeling competition were provided with a simulation environment and training data for benchmarking their models while the actual specification of the competition task was withheld. To meet this challenge, the cognitive model described here was designed and optimized for generalizability. Only two simple assumptions about human problem solving were used to explain the empirical findings of the training data. In-depth analysis of the data set prior to the development of the model led to the dismissal of correlations or other parametric statistics as goodness-of-fit indicators. A new statistical measurement based on rank orders and sequence matching techniques is being proposed instead. This measurement, when being applied to the human sample, also identifies clusters of subjects that use different strategies for the task. The acceptability of the fits achieved by the model is verified using permutation tests.

Predictive Performance and Variability of the Cardiac Anesthesia Risk Evaluation Score

2004 ◽  
Vol 100 (6) ◽  
pp. 1405-1410 ◽  
Author(s):  
Alexandre Ouattara ◽  
Michaëla Niculescu ◽  
Sarra Ghazouani ◽  
Ario Babolian ◽  
Marc Landi ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Risk Evaluation ◽  
Goodness Of Fit ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Cardiac Anesthesia ◽  
Goodness Of Fit Test ◽  
Significant Difference ◽  
Receiver Operating

Background The Cardiac Anesthesia Risk Evaluation (CARE) score, a simple Canadian classification for predicting outcome after cardiac surgery, was evaluated in 556 consecutive patients in Paris, France. The authors compared its performance to those of two multifactorial risk indexes (European System for Cardiac Operative Risk Evaluation [EuroSCORE] and Tu score) and tested its variability between groups of physicians (anesthesiologists, surgeons, and cardiologists). Methods Each patient was simultaneously assessed using the three scores by an attending anesthesiologist in the immediate preoperative period. In a blinded study, the CARE score category was also determined by a cardiologist the day before surgery, by a surgeon in the operating room, and by a second anesthesiologist at arrival in intensive care unit. Calibration and discrimination for predicting outcomes were assessed by goodness-of-fit test and area under the receiver operating characteristic curve, respectively. The level of agreement of the CARE scoring between the three physicians was then assessed. Results The calibration analysis revealed no significant difference between expected and observed outcomes for the three classifications. The areas under the receiver operating characteristic curves for mortality were 0.77 with the CARE score, 0.78 with the EuroSCORE, and 0.73 with the Tu score (not significant). The agreement rate of the CARE scoring between two anesthesiologists, between anesthesiologists and surgeons, and between anesthesiologists and cardiologists were 90%, 83%, and 77%, respectively. Conclusions Despite its simplicity, the CARE score predicts mortality and major morbidity as well the EuroSCORE. In addition, it remains devoid of significant variability when used by groups of physicians of different specialties.

scholarly journals Evaluating methods for debris-flow prediction based on rainfall in an Alpine catchment

2021 ◽  
Vol 21 (9) ◽  
pp. 2773-2789
Author(s):  
Jacob Hirschberg ◽  
Alexandre Badoux ◽  
Brian W. McArdell ◽  
Elena Leonarduzzi ◽  
Peter Molnar
Keyword(s):  
Random Forest ◽  
Debris Flow ◽  
Debris Flows ◽  
Rainfall Intensity ◽  
Multiple Scales ◽  
Learning Algorithm ◽  
Predictive Performance ◽  
Early Warning Systems ◽  
Antecedent Rainfall ◽  
Data Set

Abstract. The prediction of debris flows is relevant because this type of natural hazard can pose a threat to humans and infrastructure. Debris-flow (and landslide) early warning systems often rely on rainfall intensity–duration (ID) thresholds. Multiple competing methods exist for the determination of such ID thresholds but have not been objectively and thoroughly compared at multiple scales, and a validation and uncertainty assessment is often missing in their formulation. As a consequence, updating, interpreting, generalizing and comparing rainfall thresholds is challenging. Using a 17-year record of rainfall and 67 debris flows in a Swiss Alpine catchment (Illgraben), we determined ID thresholds and associated uncertainties as a function of record duration. Furthermore, we compared two methods for rainfall definition based on linear regression and/or true-skill-statistic maximization. The main difference between these approaches and the well-known frequentist method is that non-triggering rainfall events were also considered for obtaining ID-threshold parameters. Depending on the method applied, the ID-threshold parameters and their uncertainties differed significantly. We found that 25 debris flows are sufficient to constrain uncertainties in ID-threshold parameters to ±30 % for our study site. We further demonstrated the change in predictive performance of the two methods if a regional landslide data set with a regional rainfall product was used instead of a local one with local rainfall measurements. Hence, an important finding is that the ideal method for ID-threshold determination depends on the available landslide and rainfall data sets. Furthermore, for the local data set we tested if the ID-threshold performance can be increased by considering other rainfall properties (e.g. antecedent rainfall, maximum intensity) in a multivariate statistical learning algorithm based on decision trees (random forest). The highest predictive power was reached when the peak 30 min rainfall intensity was added to the ID variables, while no improvement was achieved by considering antecedent rainfall for debris-flow predictions in Illgraben. Although the increase in predictive performance with the random forest model over the classical ID threshold was small, such a framework could be valuable for future studies if more predictors are available from measured or modelled data.

scholarly journals Analisis Debit Puncak Sungai Lubuk Banyau Kabupaten Bengkulu Utara Menggunakan Metode Hidrograf Satuan Sintetik

2020 ◽  
Vol 12 (2) ◽  
pp. 83-90
Author(s):  
Agam Sanjaya
Keyword(s):  
Return Period ◽  
Rainfall Intensity ◽  
Peak Discharge ◽  
Peak Time ◽  
Type I ◽  
Unit Hydrograph ◽  
Distribution Method ◽  
Pearson Type ◽  
Synthetic Unit Hydrograph ◽  
Log Normal

ANALISIS DEBIT PUNCAK SUNGAI LUBUK BANYAU KABUPATEN BENGKULU UTARA DENGAN MENGGUNAKANMETODE HIDROGRAF SATUAN SINTETIK Agam Sanjaya I1), Khairul Amri II2), Muhammad Fauzi III3) 1) 2) 3)Jurusan Teknik Sipil, Fakultas Teknik UNIB Jl. W.R. Supratman, Kandang Limun, Kota Bengkulu 38371, Telp. (0736)344087e-mail: [email protected], [email protected] , [email protected] aliran sungai (DAS) Sungai Lubuk banyau merupakan salah satu DAS yang berada di Bengkulu Utara. DAS Sungai Lubuk banyau mengalir dari daerah hulu yang terletak diwilayah Kabupaten Bengkulu utara. Tujuan dari penelitian ini adalah menganalisa debit puncak rencana akibat intensitas curah hujan pada DAS Lubuk Banyau dalam menganalisis hidrologi dengan menggunakan metode Hidograf Satuan Sintetik (HSS) Gama I, HSS Nakayasu dan HSS Snyder. Berdasarkan hasil perhitungan dari penelitian ini distribusi frekuensi terhadap tiga metode curah hujan, yaitu metode ditribusi Gumbel Tipe I, Log Pearson Tipe III dan Log Normal maka metode yang digunakan untuk perhitungan curah hujan rencana pada penelitian ini adalah Metode Gumbel Tipe I dengan periode ulang 2, 5, 10, 25, 50 dan 100 tahun, yaitu 181,164 mm, 275,356 mm, 337,709 mm, 416,518 mm, 474,974 mm dan 532,998 mm. Dari hasil analisis hidrologi pada penelitian diperoleh debit puncak pada DAS Lubuk Banyau untuk periode ulang 100 tahun dengan metode HSS Snyder adalah 1531,111 m3/detik dengan waktu puncak sebesar 5 jam merupakan debit puncak yang paling besar diantara HSS Gama I dan Nakayasu. untuk hasil debit puncak dengan metode HSS Gama I adalah 776,91m3/detik dengan waktu puncak sebesar 4 jam dan HSS Nakayasu 1023,87 dengan waktu puncak 2,46 jam. Maka didapatkan tinggi permukaan air pada DAS Lubuk Banyau yaitu 1,134 m.Kata kunci: hidrograf satuan sintetik, debit puncak, gama I, nakayasu, dan snyderAbstractWatershed Lubuk Banyau is one of the watersheds in North Bengkulu. The Lubuk River watershed flows from the upstream area located in the northern Bengkulu regency. The purpose of this study is to analyze the planned peak discharge due to rainfall intensity in the Lubuk Banyau watershed in analyzing hydrology using the Synthetic Unit Hydrograph (HSS) method of Gama I, HSS Nakayasu and HSS Snyder. Based on the results of calculations from this study the frequency distribution of three rainfall methods, namely the Gumbel Type I distribution method, Pearson Type III Log and Normal Log, the method used for calculating the planned rainfall in this study is the Gumbel Type I method with a return period of 2, 5, 10, 25, 50 and 100 years, namely 181,164 mm, 275,356 mm, 337,709 mm, 416,518 mm, 474,974 mm and 532,998 mm. From the results of the hydrological analysis in the study, the peak discharge in the Lubuk Banyau watershed for a 100-year return period with the Snyder HSS method was 1531,111 m3 / second with a peak time of 5 hours being the largest peak discharge between Gama I and Nakayasu HSS. for the peak discharge using the HSS Gama I method is 776.91m3 / sec with a peak time of 4 hours and Nakayasu HSS of 1023.87 with a peak time of 2.46 hours. Then the water level obtained at the Lubuk Banyau watershed is 1,134 m.Keywords: synthetic unit hydrograph, peak discharge gama I, nakayasu, and snyder.

scholarly journals Evaluation of nine reference evapotranspiration models in Traghen region, Southwest of Libya

2020 ◽  
Vol 35 (3) ◽  
pp. 225-246
Author(s):  
Ali Othman Alghusni
Keyword(s):  
Goodness Of Fit ◽  
Reference Evapotranspiration ◽  
T Test ◽  
Mean Bias Error ◽  
Radiation Model ◽  
Study Region ◽  
The Third ◽  
Statistical Measures ◽  
Pearson Type ◽  
Significant Difference

  This research was carried out to evaluate nine reference evapotranspiration, and find out an alternative models to the standard FAO Penman-Monteith model in Traghen region southwest of Libya. The models applied were standard FAO Penman-Monteith, FAO Radiation, FAO Blaney-Criddle, Hargreaves-Samani, Priestley-Taylor, Makkink, Turc, Thornthwaite, Kharrufa and McCloud.  The models were compared with the FAO Penman-Monteith model using root mean square error (RMSE) , mean bias error (MBE), Pearson type goodness of fit index (R2), refined index of agreement (dr), modeling efficiency (Ef) and t-test.  Results showed that FAO Radiation and FAO Blaney-Criddle models overestimated ETo by values ranged from 0.64% to 16.06%.  However, Kharrufa and McCloud models overestimated ETo in some months and underestimated ETo in some other months.  Whereas, the other models underestimated ETo by values ranged from -86.69 % to -6.02%.  The FAO radiation model gave the highest dr (0.892) and Ef (0.951) values, and the lowest RMSE (0.534 mm/day), indicating that this model was the best alternative to the FAO Penman-Monteith model in the study region, followed by FAO Blaney-Criddle model with dr value of 0.851, Ef value of 0.878, and RMSE value of 0.845 mm/day.  In addition, FAO radiation model showed the second best R2 and MBE values at 0.993 and 0.473, respectively  Also, FAO Blaney-Criddle model showed the third best R2 value at 0.989.  Therefore, FAO radiation model is ranked the first and FAO Blaney-Criddle model is ranked the second.  According to the statistical measures stated above, Turc model ranked the third, Kharrufa model ranked forth.  Models of Hargreaves-Samani, Makkink, Priestley-Taylor, McCloud and Thornthwaite ranked fifth, sixth, seventh, eighth and ninth, respectively.  T-test analysis at 5% level of significance indicated that there is a significant difference between the FAO Penman-Monteith model and all models except Kharrufa and McCloud models.

scholarly journals Modelling Rainfall Intensity by Optimization Technique in Abeokuta, South-West, Nigeria

2019 ◽  
pp. 1-10
Author(s):  
A. O. David ◽  
Ify L. Nwaogazie ◽  
J. C. Agunwamba
Keyword(s):  
Probability Distribution ◽  
Goodness Of Fit ◽  
Rainfall Intensity ◽  
Mean Squared Error ◽  
Optimization Technique ◽  
Distribution Models ◽  
Goodness Of Fit Test ◽  
Maximum Rainfall ◽  
Pearson Type ◽  
Type 3

The design of water resources engineering control structures is best achieved with adequate estimation of rainfall intensity over a particular catchment. To develop the rainfall intensity, duration and frequency (IDF) models, 25 year daily rainfall data were collected from Nigerian Meteorological Agency (NIMET) Abuja for Abeokuta. The annual maximum rainfall amounts with durations of 5, 10, 15, 20, 30, 45, 60, 90, 120, 180, 240, 300 and 420 minutes were extracted and subjected to frequency analysis using the Excel Optimization Solver wizard. Specific and general IDF models were developed for return periods of 2, 5, 10, 25, 50 and 100 years using the Gumbel Extreme Value Type -1 and Log Pearson Type -3 distributions. The Anderson-Darling goodness of fit test was used to ascertain the best fit probability distribution. The R2 values range from 0.973 – 0.993 and the Mean Squared Error, MSE from 84.49 – 134.56 for the Gumbel and 0.964 – 0.997 with MSE of 42.88 – 118.68 for Log Pearson Type -3 distribution, respectively. The probability distribution models are recommended for the prediction of rainfall intensities for Abeokuta metropolis.

scholarly journals ANNUAL FLOOD ANALYSIS OF CISANGGARUNG WATERSHED IN CIREBON REGENCY

Neutron ◽  
2019 ◽  
Vol 18 (2) ◽  
pp. 42-50
Author(s):  
Abdul Muin ◽  
Jantiara Eka Nandiasa
Keyword(s):  
Return Period ◽  
Flood Control ◽  
Quantitative Methods ◽  
Annual Maximum ◽  
West Java ◽  
Flood Discharge ◽  
Pearson Type ◽  
Flood Analysis ◽  
Pearson Type Iii Distribution ◽  
Rain Data

Cisanggarung River, a river in West Java Province, often experiences flooding. This study aims to discuss the magnitude of annual flood discharge that may occur in the Cisanggarung watershed. Rain data at each station in the Cisanggarung watershed from 2005 to 2017 were analyzed using descriptive-quantitative methods. Return period flood discharge 2, 5, 10, 20, and 50 years were compared to 2-yearly and monthly flood discharge. The results showed that the data followed the Log-Pearson Type III distribution. The return period flood discharge is: Q2= 181.518 m3/s, Q5 = 242.498 m3/s, Q10 = 283.109 m3/s, Q20 = 316.534 m3/s, Q50 = 373.369 m3/s, Q100 = 412.425 m3/s, Q200 = 452.013 m3/s, dan Q1000 = 546.683 m3/s by using the Nakayasu method. Based on the 2 annual maximum daily rains, 2005, 2007, 2009-2010, 2015, 2009-2017 has the potential to flood Q2, 2012 has the potential to flood Q5, and 2017 has the potential to flood Q10. According to maximum 2-daily monthly rainfall, in 2005-2007, January-April and November have the potential to flood Q2. December has the potential to flood Q10. These results are useful for flood control in the region to be more effective and accurate.

scholarly journals Study on the Classification of Urban Waterlogging Rainstorms and Rainfall Thresholds in Cities Lacking Actual Data

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3328
Author(s):  
Bingyan Ma ◽  
Zening Wu ◽  
Huiliang Wang ◽  
Yuan Guo
Keyword(s):  
Return Period ◽  
Flood Control ◽  
Rainfall Intensity ◽  
Influencing Factor ◽  
Extreme Rainfall ◽  
Observation Data ◽  
Urban Flood ◽  
Storm Water Management Model ◽  
Different Characteristics

Extreme rainfall is the main influencing factor of urban waterlogging. Different types of rainfall often have different characteristics of waterlogging. In order to establish a more accurate urban flood control system, it is necessary to classify waterlogging rainstorms and divide their thresholds. This study proposes a method for applying web crawlers to identify waterlogging rainfall in cities lacking waterlogging observation data and classifying them using the rainfall intensity–duration curves. By selecting appropriate duration thresholds and return period, waterlogging rainstorms are divided into rainfall intensity waterlogging (IW), rainfall amount of waterlogging (AW), combined waterlogging (CW) and no waterlogging (NW). In the application of Zhengzhou City, China, the urban flood control standard and the rainfall time distribution characteristics are used as the basis for the selection of the return period and duration thresholds, and the storm water management model (SWMM) is constructed to simulate the 4 kinds of rainfall characteristics of waterlogging, which is similar to actual situations. It proves that the method is suitable for the classification and thresholds division of different waterlogging rainfall in cities. The results show that the best duration thresholds in Zhengzhou are 20 min (M20) and 60 min (M60), and the best return period standard is 2 a. The thresholds for the 4 types of waterlogging rainstorm are: M20 ≥ 26.47 mm, M60 ≥ 43.80 mm, CW; M20 ≥ 26.47 mm, M60 < 43.80 mm, IW; M20 < 26.47 mm, M60 ≥ 43.80 mm, AW; M20 < 26.47 mm and M60 < 43.80 mm, No waterlogging.

scholarly journals Regional Frequency Analysis of Maximum Monthly Rainfall in Haryana State of India Using L-Moments

2021 ◽  
Author(s):  
Mohit Nain ◽  
B. K. Hooda
Keyword(s):  
Return Period ◽  
Goodness Of Fit ◽  
Regional Distribution ◽  
Rain Gauge ◽  
Monthly Rainfall ◽  
Regional Frequency Analysis ◽  
Goodness Of Fit Test ◽  
Pearson Type ◽  
L Moments ◽  
Regional Frequency

The paper aims to select the appropriate regional frequency distribution for the maximum monthly rainfall and estimation of quantiles using L-moments for the 27 rain gauge stations in Haryana. These 27 rain gauge stations were grouped into three homogeneous regions (Region-1, Region-2, and Region-3) using Ward’s method of cluster analysis. To confirm the homogeneity of each region, L-moments based measure of heterogeneity was used. For each homogeneous region, a regional distribution was selected with the help of the L-moments ratio diagram and goodness-of-fit test. Results of the goodness-of-fit test and L-moments ratio diagram indicated that Generalized Logistic and Generalized Extreme Value distributions were best- fitted regional frequency distributions for the Region-1 and Region-2 respectively while for Region-3, Pearson Type-3) was best-fitted distribution. The quantiles for each region were calculated and the regional growth curves were developed. The accuracy measurements were determined using Monte Carlo simulations for the regional quantiles. Results of simulations showed that uncertainty in regional quantiles measured by Root Mean Square Error value and 90 percent error limits were small when the return period was low but uncertainty in quantiles increases as the return period increases.

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