Interpolation of Regionalized Intensity Duration Frequency (IDF) Estimates based on the observed precipitation data of Baden Wurttemberg (BW), Germany

2021 ◽  
Author(s):  
Bushra Amin ◽  
András Bárdossy
Keyword(s):  
Mean Square Error ◽  
Hydrological Cycle ◽  
Information Criteria ◽  
Regional Frequency Analysis ◽  
Mean Square ◽  
Time Duration ◽  
Ungauged Sites ◽  
Marquardt Algorithm ◽  
Idf Curves ◽  
Intensity Duration Frequency

<p>This study is intended to carry out the spatial mapping with ordinary Kriging (OK) of regional point Intensity Duration Frequency (IDF) estimates for the sake of approximation and visualization at ungauged location. Precipitation IDF estimates that offer us valuable information about the frequency of occurrence of extreme events corresponding to different durations and intensities are derived through the application of robust and efficient regional frequency analysis (RFA) based on L-moment algorithm. IDF curves for Baden Wrttemberg (BW) are obtained from the long historical record of daily and hourly annual maximum precipitation series (AMS) provided by German Weather Service from 1960-2020 and 1949-2020 respectively under the assumption of stationarity. One of the widely used Gumbel (type 1)  distribution is applied for IDF analysis because of its suitability for modeling maxima. The uncertainty in IDF curves is determined by the bootstrap method and are revealed in the form of the prediction and confidence interval for each specific time duration on graph. Five metrics such as root mean square error (RMSE), coefficient of determination (R²), mean square error (MSE), Akaike information criteria (AIC) and Bayesian information criteria (BIC) are used to assess the performance of the employed IDF equation. The coefficients of 3-parameteric non-linear IDF equation is determined for various recurrence interval by means of Levenberg–Marquardt algorithm (LMA), also referred to as damped least square (DLS) method. The estimated coefficients vary from location to location but are insensitive to duration. After successfully determining the IDF parameters for the same return period, parametric contour or isopluvial maps can be generated using OK as an interpolation tool with the intention to provide estimates at ungauged locations. These estimated regional coefficients of IDF curve are then fed to the empirical intensity frequency equation that may serve to estimate rainfall intensity for design purposes for all ungauged sites. The outcomes of this research contribute to the construction of IDF-based design criteria for water projects in ungauged sites located anywhere in the state of BW.</p><p>In conclusion, we conducted IDF analysis for the entire state of BW as it is considered to be more demanding due to the increased impact of climate change on the intensification of hydrological cycle as well as the expansion of urban areas rendering watershed less penetrable to rainfall and run-off, the better understanding of spatial heterogeneity of intense rainfall patterns for the proposed domain.</p>

scholarly journals Indian COVID-19 dynamics: Prediction using autoregressive integrated moving average modelling

2021 ◽  
Vol 52 (1) ◽  
pp. 6-14
Author(s):  
Amit Tak ◽  
Sunita Dia ◽  
Mahendra Dia ◽  
Todd Wehner
Keyword(s):  
Root Mean Square Error ◽  
Real Time ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Moving Average ◽  
Information Criteria ◽  
Evidence Based ◽  
Mean Square ◽  
Autoregressive Integrated Moving Average ◽  
Akaike Information Criteria

Background: The forecasting of Coronavirus Disease-19 (COVID-19) dynamics is a centrepiece in evidence-based disease management. Numerous approaches that use mathematical modelling have been used to predict the outcome of the pandemic, including data-driven models, empirical and hybrid models. This study was aimed at prediction of COVID-19 evolution in India using a model based on autoregressive integrated moving average (ARIMA). Material and Methods: Real-time Indian data of cumulative cases and deaths of COVID-19 was retrieved from the Johns Hopkins dashboard. The dataset from 11 March 2020 to 25 June 2020 (n = 107 time points) was used to fit the autoregressive integrated moving average model. The model with minimum Akaike Information Criteria was used for forecasting. The predicted root mean square error (PredRMSE) and base root mean square error (BaseRMSE) were used to validate the model. Results: The ARIMA (1,3,2) and ARIMA (3,3,1) model fit best for cumulative cases and deaths, respectively, with minimum Akaike Information Criteria. The prediction of cumulative cases and deaths for next 10 days from 26 June 2020 to 5 July 2020 showed a trend toward continuous increment. The PredRMSE and BaseRMSE of ARIMA (1,3,2) model were 21,137 and 166,330, respectively. Similarly, PredRMSE and BaseRMSE of ARIMA (3,3,1) model were 668.7 and 5,431, respectively. Conclusion: It is proposed that data on COVID-19 be collected continuously, and that forecasting continue in real time. The COVID-19 forecast assist government in resource optimisation and evidence-based decision making for a subsequent state of affairs.

scholarly journals Web-Based Tool for the Development of Intensity Duration Frequency Curves under Changing Climate at Gauged and Ungauged Locations

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1243 ◽  
Author(s):  
Andre Schardong ◽  
Slobodan P. Simonovic ◽  
Abhishek Gaur ◽  
Dan Sandink
Keyword(s):  
Climate Models ◽  
Climatic Conditions ◽  
Web Based ◽  
Matching Algorithm ◽  
Resources Management ◽  
Ungauged Sites ◽  
Land Mass ◽  
Idf Curves ◽  
Intensity Duration Frequency ◽  
Frequency Curves

Rainfall Intensity–Duration–Frequency (IDF) curves are among the most essential datasets used in water resources management across the globe. Traditionally, they are derived from observations of historical rainfall, under the assumption of stationarity. Change of climatic conditions makes use of historical data for development of IDFs for the future unreliable, and in some cases, may lead to underestimated infrastructure designs. The IDF_CC tool is designed to assist water professionals and engineers in producing IDF estimates under changing climatic conditions. The latest version of the tool (Version 4) provides updated IDF curve estimates for gauged locations (rainfall monitoring stations) and ungauged sites using a new gridded dataset of IDF curves for the land mass of Canada. The tool has been developed using web-based technologies and takes the form of a decision support system (DSS). The main modifications and improvements between version 1 and the latest version of the IDF_CC tool include: (i) introduction of the Generalized Extreme value (GEV) distribution; (ii) updated equidistant matching algorithm (QM); (iii) gridded IDF curves dataset for ungauged location and (iv) updated Climate Models.

Estimating IDF-Relations consistently using a duration-dependent GEV with spatial covariates

2020 ◽  
Author(s):  
Jana Ulrich ◽  
Madlen Peter ◽  
Oscar E. Jurado ◽  
Henning W. Rust
Keyword(s):  
Extreme Precipitation ◽  
Probability Distributions ◽  
Skill Score ◽  
Exceedance Probability ◽  
Ungauged Sites ◽  
Extreme Precipitation Events ◽  
One Step ◽  
Idf Curves ◽  
Intensity Duration Frequency ◽  
A Site

<p>Intensity-Duration-Frequency (IDF) Curves are a popular tool in Hydrology for estimating the properties of extreme precipitation events. They describe the relationship between rainfall intensity and duration for a given non-exceedance probability (or frequency). For a site where precipitation measurements are available, these curves can be estimated consistently over durations using a duration-dependent GEV (d-GEV, after Koutsoyiannis et al. 1998). In this approach, the probability distributions are modeled simultaneously for all durations.</p><p>Additionally, we integrate covariates to describe the spatial variability of the d-GEV parameters so that we can model the distribution of extreme precipitation for a range of durations and locations in one step. Thus IDF Curves can be estimated even at ungauged sites. Further advantages are parameter reduction and more efficient use of the available data. We use the Quantile Skill Score to investigate under which conditions this method leads to an improved estimate compared to the single-site approach and to evaluate the performance at ungauged sites.</p>

scholarly journals An Artificial Neural Network Model for Highway Accident Prediction: A Case Study of Erzurum, Turkey

2015 ◽  
Vol 27 (3) ◽  
pp. 217-225 ◽  
Author(s):  
Muhammed Yasin Çodur ◽  
Ahmet Tortum
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Traffic Accidents ◽  
Model Parameters ◽  
Mean Square ◽  
Ann Model ◽  
Accident Prediction ◽  
Marquardt Algorithm ◽  
Artificial Neural

This study presents an accident prediction model of Erzurum’s Highways in Turkey using artificial neural network (ANN) approaches. There are many ANN models for predicting the number of accidents on highways that were developed using 8 years with 7,780 complete accident reports of historical data (2005-2012). The best ANN model was chosen for this task and the model parameters included years, highway sections, section length (km), annual average daily traffic (AADT), the degree of horizontal curvature, the degree of vertical curvature, traffic accidents with heavy vehicles (percentage), and traffic accidents that occurred in summer (percentage). In the ANN model development, the sigmoid activation function was employed with Levenberg-Marquardt algorithm. The performance of the developed ANN model was evaluated by mean square error (MSE), the root mean square error (RMSE), and the coefficient of determination (R2). The model results indicate that the degree of vertical curvature is the most important parameter that affects the number of accidents on highways.

scholarly journals Regionalization of Rainfall Intensity-Duration-Frequency (IDF) Curves With L-Moments Method Using Neural Gas Networks

2021 ◽  
Author(s):  
Mohammadreza Mahmoudi ◽  
Saeid Eslamian ◽  
Saeid Soltani
Keyword(s):  
Flood Control ◽  
Rain Gauge ◽  
Single Site ◽  
Regional Frequency Analysis ◽  
Measuring Device ◽  
Gas Networks ◽  
Neural Gas ◽  
L Moments ◽  
Idf Curves ◽  
Intensity Duration Frequency

Abstract Floods are one of the most frequent and destructive natural events which lead to lots of human and financial losses with damage to the houses, farms, roads, and other buildings. Intensity-duration-frequency (IDF) curves are the main and practical tools that have been used for flood control studies including the design of the water structures. In many cases, there is not any measuring device at the desired place or their information are not useful if there is any available. In this case, it is not possible to extract these curves through the conventional methods. Regionalizing the IDF curves is a method that has solved the issues mentioned in the common methods. In this research, the regionalized IDF curves are extracted in Khozestan province, Iran using 21 rain gauge stations through L-moments and neural gas networks. Clustering is one of the most effective steps and a prerequisite for regional frequency analysis (RFA) that divides the region and existing stations into hydrologically homogenous regions. In this study, clustering is done using two new models named neural gas (NG) and growing neural gas (GNG) network. Comparing the regional IDF curves with single site curves, it was found that neural gas network models had a more accurate performance and higher efficiency so that they had the lowest estimate error amount among other models. Also, due to the acceptable difference between regional and single site curves, the efficiency of L-Moments in RFA was evaluated as appropriate.

scholarly journals On the value of river network information in regional frequency analysis

2020 ◽  
Author(s):  
Kichul Jung ◽  
Taha B. M. J. Ouarda ◽  
Prashanth R. Marpu
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Frequency Analysis ◽  
River Network ◽  
Superior Performance ◽  
Regional Frequency Analysis ◽  
Mean Square ◽  
Process Data ◽  
Regional Frequency

AbstractRegional frequency analysis (RFA) is widely used in the design of hydraulic structures at locations where streamflow records are not available. RFA estimates depend on the precise delineation of homogenous regions for accurate information transfer. This study proposes new physiographical variables based on river network features and tests their potential to improve the accuracy of hydrological feature estimates. Information about river network types is used both in the definition of homogenous regions and in the estimation process. Data from 105 river basins in arid and semi-arid regions of the USA were used in our analysis. Artificial neural network ensemble models and canonical correlation analysis were used to produce flood quantile estimates, which were validated through tenfold cross- and jackknife validations. We conducted analysis for model performance based on statistical indices, such as the Nash–Sutcliffe Efficiency, root mean square error, relative root mean square error, mean absolute error, and relative mean bias. Among various combinations of variables, a model with 10 variables produced the best performance. Further, 49, 36, and 20 river networks in the 105 basins were classified as dendritic, pinnate, and trellis networks, respectively. The model with river network classification for the homogenous regions appeared to provide a superior performance compared with a model without such classification. The results indicated that including our proposed combination of variables could improve the accuracy of RFA flood estimates with the classification of the network types. This finding has considerable implications for hydraulic structure design.

New order selection technique using information criteria applied to SISO and MIMO systems predistortion

2013 ◽  
Vol 5 (2) ◽  
pp. 123-131 ◽  
Author(s):  
M.V. Amiri ◽  
S.A. Bassam ◽  
M. Helaoui ◽  
F.M. Ghannouchi
Keyword(s):  
Mean Square Error ◽  
Mimo Systems ◽  
Cost Minimization ◽  
Information Criterion ◽  
Information Criteria ◽  
New Order ◽  
Order Selection ◽  
Mean Square ◽  
Model Order Selection ◽  
Selection Technique

This paper presents a new order selection technique of matrix memory polynomial technique that models the nonlinearities of single-branch and multi-branch transmitters. The new criteria take into account the complexity of the model in addition to its mean-square error in the selection criteria. The quasi-convexity of the proposed criteria was proven in this work. By using this proposed Akaike information criterion (AIC) and Bayesian information criterion (BIC) criteria, the model order selection was cast as a cost minimization problem. To minimize the criteria, modified gradient descent and simulated annealing algorithms were utilized which resulted in a considerable reduction in the number of search iterations. The performances of the criteria were shown by comparing the normalized mean square error (NMSE) of a higher-order model and the optimum model. It has been shown that the NMSE difference is <0.5 dB, but the complexity is much smaller.

scholarly journals Estimation of rainfall based on remote sensing and observation fields in Jaru Biological Reserve at the Brazilian Amazonian forest

2021 ◽  
Vol 5 (2) ◽  
pp. 1-12
Author(s):  
KEYLYANE SANTOS DA SILVA ALVES ALVES ◽  
LUCIANA SANCHES ◽  
NARA LUÍSA REIS DE ANDRADE ◽  
GRACYELI SANTOS SOUZA GUARIENTE ◽  
PETER ZEILHOFER
Keyword(s):  
Mean Square Error ◽  
Amazon Basin ◽  
Hydrological Cycle ◽  
Probability Of Detection ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Mean Square ◽  
Surface Precipitation ◽  
The Mean ◽  
Biological Reserve

The Amazon basin, with a drainage area of about 6 million km2, is the largest drainage basin in the world, consequently the accurate measuring the rainfall dynamics at high spatial and temporal resolution is essential for a better understanding of the hydrological cycle. So, we validated rainfall estimates from the Tropical Rainfall Measuring Mission (TRMM) satellite using surface precipitation data collected from 2004 to 2012. Rainfall data came from the Jaru Biological Reserve meteorological station, located to the east in the state of Rondônia, Brazil, and was compared with the estimates of the algorithms 3B42 V7 and 3B43 V7 of the product TRMM. Statistical analysis was based on indices of continuous variables such as the Spearman correlation coefficient (ρ), the square root mean square error normalized by the mean of the observed values ​​(NRMSE), the mean square error (RMSE), the error (ERV) and some categorical indices such as probability of detection (POD), False Alarm (FAR) and success rate (CSI) between the daily and monthly precipitation observed data and the estimated precipitation data. The 3B43 V7 precipitation estimates were broadly representative of surface observations, but underestimated precipitation in the wet season and overestimated precipitation in the dry season. The 3B42 V7 product performed poorly and does not generate a robust representation of surface precipitation.

scholarly journals Prediksi Tingkat Kriminalitas Menggunakan Jaringan Syaraf Tiruan Backpropagation: Algoritma Levenberg Marquardt di Kota Manado Berbasis Sistem Informasi Geografi

d'CARTESIAN ◽  
2014 ◽  
Vol 3 (1) ◽  
pp. 89
Author(s):  
Lindsay Mokosuli ◽  
Winsy Weku ◽  
Luther Latumakulita
Keyword(s):  
Mean Square Error ◽  
Crime Rate ◽  
Crime Rates ◽  
Learning Rate ◽  
Mean Square ◽  
Crime Mapping ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Data Theft ◽  
The City

Abstract The information needs about crime rate in Manado become a starfing point to conduct this research. It has been done to predict the crime rate in the city of Manado using Levenberg Marquardt algorithm with training to determine the value of learning rate and momentum constant based on the value of the smallest Mean Square Error. Then do the mapping with Crime Mapping and perform cluster the predicted results to see the effect of the crime rate among adjacent districts. The data used is the data theft in the city of Manado in 2007 until 2012. The selected target is the data theft in August 2012 until December 2012. Bunaken district obtain predictive results=[1.9997, 0.1667, 2.0000, 0.1667, 0.0000], Mapanget district obtain predictive results=[25.9995, 25.9997, 25.9801, 8.0335, 16.0265], Tuminting district obtain predictive results=[2.0000, 2.3786, 2.0000, 3.1020, 3.1020], Singkil district obtain predictive results=[6.6716, 6.1388, 5.6570, 5.4000, 3.0035], Wenang district obtain predictive results=[3.1316, 4.0677, 3.0000, 9.3971, 9.8967], Tikala district obtain predictive results=[0, 1, 0, 0, 0], Wanea district obtain predictive results=[6.8911, 6.6811, 1.4788, 5.8941, 6.9207], Sario district obtain predictive results = [1.0000, 1.8381, 1.0000, 6.0314, 9.0000], Malalayang district obtain the predicted result=[44.0000, 38.1828, 43.1787, 38.1935, 38.1789]. By visualization of mapping results predicted, on target August 2012 until October 2012, Mapanget and Malalayang districts have the highest crime rates. While the target of November 2012 and December 2012, Malalayang district have the highest crime rates. Keywords: Levenberg Marquardt Algorithm, Geographic Information System, Spatial Data.   Abstrak Perlunya informasi tentang tingkat kriminalitas di kota Manado, maka telah dilakukan penelitian untuk memprediksi tingkat kriminalitas di kota Manado menggunakan algoritma Levenberg Marquardt dengan melakukan pelatihan untuk menentukan nilai learning rate dan momentum constant berdasarkan nilai Mean Square Error terkecil. Kemudian dilakukan pemetaan dengan Crime Mapping serta melakukan cluster terhadap hasil prediksi untuk melihat pengaruh tingkat kriminalitas antar kecamatan yang saling berdekatan. Data yang digunakan adalah data pencurian di kota Manado tahun 2007 sampai tahun 2012. Target yang dipilih adalah data pencurian bulan Agustus 2012 sampai Desember 2012. Kecamatan Bunaken memperoleh hasil prediksi=[1.9997, 0.1667, 2.0000, 0.1667, 0.0000], Kecamatan Mapanget memperoleh hasil prediksi=[25.9995,  25.9997, 25.9801, 8.0335, 16.0265], kecamatan Tuminting memperoleh hasil prediksi=[2.0000, 2.3786, 2.0000, 3.1020, 3.1020], kecamatan Singkil memperoleh hasil prediksi= [6.6716, 6.1388, 5.6570, 5.4000, 3.0035], kecamatan Wenang memperoleh hasil prediksi= [3.1316, 4.0677, 3.0000, 9.3971, 9.8967], kecamatan Tikala memperoleh hasil prediksi= [0, 1, 0, 0, 0], kecamatan Wanea memperoleh hasil prediksi= [6.8911, 6.6811, 1.4788, 5.8941, 6.9207], kecamatan Sario memperoleh hasil prediksi=[1.0000, 1.8381, 1.0000, 6.0314, 9.0000], dan kecamatan Malalayang memperoleh hasil prediksi= [44.0000, 38.1828, 43.1787, 38.1935, 38.1789]. Secara visualisasi pemetaan hasil prediksi, pada target Agustus 2012 sampai Oktober 2012, kecamatan  Malalayang dan Mapanget memiliki tingkat kriminalitas tertinggi. Sedangkan pada target November 2012, dan Desember 2012, kecamatan Malalayang memiliki tingkat kejahatan tertinggi.   Kata kunci: Algoritma Levenberg Marquardt, Sistem Informasi Geografi, Data Spasial.

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