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Deriving rainfall intensity–duration–frequency (IDF) curves and testing the best distribution using EasyFit software 5.5 for Kut city, Iraq

  • Mohammed S. Shamkhi , Marwaa K. Azeez EMAIL logo and Zahraa H. Obeid
Published/Copyright: December 5, 2022
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Abstract

The intensity of rainfall can be considered as an essential factor in designing and operating hydraulic structures. The intensity–duration–frequency (IDF) curve is used for designing hydraulic projects such as drainage networks, road culverts, bridges, and many other hydraulic structures. In the field of water resources engineering, IDF curve is dependent widely on the plan, designing, and operating the project. Additionally, it can be used for different flood engineering structures. The purpose of this research is to get the frequency of the intensity of rain duration for Al KUTcity, Iraq, and find curves. Three essential techniques of frequency analysis (Gumbel distribution, lognormal, and log Pearson Type III) were depended to formulate this relationship based on data of rainfall intensity during the period between 1992 and 2019. Distribution methods involving lognormal, Gumbel, and log Pearson Type III were applied by Indian Meteorological Department (IMD) for short periods of 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 12, and 24 h with 2, 5, 10, 25, 50, and 100 years return periods. The results showed that rainfall intensity reduced as the duration of the storm increased, and if the return period of the rainfall was large, rainfall of any specific duration showed a higher intensity. Using EasyFit 5.5 software, for all durations, the lognormal probability distribution showed the best fit for the data group and estimated intensities of precipitation for return periods of 2, 5, 10, 25, 50, and 100 years. According to the obtained results, one can notice that the intensity of rainfall increased with the increment in return periods, but decreased with the increment in duration. The resulting IDF models could be used to improve accuracy and results.

1 Introduction

The curve that links the intensity of rainfall with its duration and occurrence frequency is called the intensity-duration-frequency (IDF) curve [1,2]. Urbanization, which occurred due to the high increase in population, and development of infrastructure have made several regions in Iraq vulnerable to severe flooding risks [3]. Economical and safe flood control structures can be designed using IDF curves. It represents a mathematical relationship that relates the return period, intensity, and duration of rainfall [4]. Studies on IDF relationship of rainfall have obtained special focus during the past few decades [5]. Hussein formulated IDF empirical relationship that can be used in the province of Karbala. Various statistical distributions were compared. It was concluded that the best method among the studied methods was log Pearson type III (LPT III) [6].The purpose of Zup and others was to create IDF precipitation curves for Mumbai under changing hydrologic conditions, and it was discovered that Colaba’s precipitation intensity is 112.48 mm/h over the return period of 100 years. Even with the significant rainfall on July 26, 2005 in Mumbai, the curves of IDF that was created using the updated formula gave acceptable results in hydrological conditions shifting and were consistent [7]. Al-Awadi assessed curves of IDF and formulated a relationship for duration and intensities for a group of intervals of recurrence for Baghdad city. Log Pearson Type III, Gumbel method, and lognormal distribution were used, and based on the obtained results, small priority was given to LPT III distribution [8]. Wambua assessed IDF rainfall curves for Kenya’s tropical river basin depending on empirical formulas to obtain rainfall of short durations, they ranged between 1 and 12 h, and empirical models for IDF curves were derived by regression analysis [9]. Muhammad and others presented a study in Kano State aiming at producing new IDF curves that might be used for safe and cost-effective hydraulic design. The maximum daily precipitation in Kano was divided by precipitation over shorter periods using the reduction equation (Indian Meteorological Department, IMD). The optimal dataset for all eras of use was discovered to be an unstable logarithmic probability distribution. Easy Fit 5.5 software is used to estimate the best intensities, first the most suitable for the area in question [10]. This research also adopted the IMD reduction equation to estimate the precipitation for shorter periods. However, several relationships are set up for IDF curves for different locations around the world. The primary aim of this work is to collect rainfall data for Kut city to develop IDF curves for different return periods. For this purpose, three different statistical distribution methods were applied, and the probability distribution function of the daily maximum precipitation data was verified by software of EasyFit 5.5. These equations and curves are very important in designing drainage systems in urban areas, for example, canals, storm sewers, and any different hydraulic projects.

2 Area of study and data collection

The study area is Wasit Province, which is located in eastern Iraq, southeast of Baghdad city. It is located along Tigris in the midway between Basra and Baghdad cities. The total area of Wasit Province is 17,153 km2 (6,623 square miles). The topography of the area is approximately flat. The population of Wasit Province is approximately 1,450,000 inhabitants with about 3% annual growth rate (according to Wasit Statistic Department) (Figure 1).

Figure 1 
               The studied zone location.
Figure 1

The studied zone location.

2.1 Study area climate

The weather or climate of Wasit Province is hot as in a desert. Most rainfall occurs during the winter. The average annual temperature in Kut is 24.6°C (76.3°F). The mean speed of wind is approximately 2.0 m/s, the minimum value of total solar radiation is 7.7 MJ/M² in December, while the maximum value is 23.9 MJ/m²/m in June. The mean monthly temperature ranges from 37.6°C in August to 11.5°C in January, with variation in temperature. The monthly minimum and maximum relative humidity are about 14 and 62%, respectively. The humidity decreases in the summer due to high temperature, while increases in winter because of frequent rainfall, as shown in Figure 2 [11].

Figure 2 
                  Summary of the climate data from 2013 to 2018 for Wasit Province.
Figure 2

Summary of the climate data from 2013 to 2018 for Wasit Province.

2.2 Annual rainfall data

About 103.6 mm (4.08 in) of precipitation falls annually. Rainfall occurs during winter and spring seasons and differs from year to another. The watershed of Wasit Governorate experienced approximately the same amount of rainfall; however, it has some spatial differences in the distribution of other hydrological components such as runoff. Figure 3 shows that the average annual precipitation during 1992–2019 was 13.3 mm. The lowest value of precipitation was 4.8 mm in 2017. However, this value started to rise in recent years, reaching its highest value in 2018 (33.4 mm) and (20.5 mm) in 2019, the highest value of precipitation in Wasit Governorate.

The precipitation data involve the maximum values of daily precipitation from 1992 to 2019 [20]. The maximum daily precipitation was divided into small periods every half hour are 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 12, and 24 h of precipitation. Values of precipitation are obtained depending on the empirical reduction formula prepared by IMD [10].

(1) P ( t ) = P ( 24 ) t 24 ( 1 / 3 ) ,

where P(t) represents the required depth of precipitation at duration of t-hour (in mm), P (24) represents the value of daily precipitation (in mm), and t represents the rainfall duration (in h) for which the depth of precipitation is required. Table 1 shows the rainfalls of shorter duration that were derived from maximum daily precipitation throughout year.

Table 1

The required rainfall depth P ( t ) for t-hour duration (in mm)

Year P (mm) 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 6 12 24
1992 40.3 11.1 14.0 16.0 17.6 19 20.2 21.2 22.2 23.1 23.9 25.4 32.0 40.3
1993 49.0 13.5 17.0 19.5 21.4 23.1 24.5 25.8 27.0 28.1 29.1 30.9 38.9 49.0
1994 80.8 22.2 28.0 32.1 35.3 38.0 40.4 42.5 44.5 46.3 47.9 50.9 64.1 80.8
1995 53.2 14.6 18.4 21.1 23.2 25.0 26.6 28.0 29.3 30.5 31.5 33.5 42.2 53.2
1996 89.2 24.5 30.9 35.4 39.0 42.0 44.6 47.0 49.1 51.1 52.9 56.2 70.8 89.2
1997 68.8 18.9 23.9 27.3 30.1 32.4 34.4 36.2 37.9 39.4 40.8 43.3 54.6 68.8
1998 45.3 12.5 15.7 18.0 19.8 21.3 22.7 23.8 24.9 25.9 26.9 28.5 36.0 45.3
1999 47.6 13.1 16.5 18.9 20.8 22.4 23.8 25.1 26.2 27.2 28.2 30.0 37.8 47.6
2000 42.9 11.8 14.9 17.0 18.7 20.2 21.5 22.6 23.6 24.6 25.4 27.0 34.1 42.9
2001 28.3 7.8 9.8 11.2 12.4 13.3 14.2 14.9 15.6 16.2 16.8 17.8 22.5 28.3
2002 50.9 14.0 17.7 20.2 22.2 24.0 25.5 26.8 28.0 29.1 30.2 32.1 40.4 50.9
M*
2011 51.3 14.1 17.8 20.4 22.4 24.1 25.7 27.0 28.2 29.4 30.4 32.3 40.7 51.3
2012 50.3 13.8 17.4 20.0 22.0 23.7 25.2 26.5 27.7 28.8 29.8 31.7 39.9 50.3
2013 87.5 24.1 30.3 34.7 38.2 41.2 43.8 46.1 48.2 50.1 51.8 55.1 69.5 87.5
2014 54.2 14.9 18.8 21.5 23.7 25.5 27.1 28.5 29.8 31.0 32.1 34.1 43.0 54.2
2015 61 16.8 21.2 24.2 26.6 28.7 30.5 32.1 33.6 34.9 36.2 38.4 48.4 61.0
2016 38.3 10.5 13.3 15.2 16.7 18.0 19.2 20.2 21.1 21.9 22.7 24.1 30.4 38.3
2017 29.3 8.1 10.2 11.6 12.8 13.8 14.7 15.4 16.1 16.8 17.4 18.5 23.3 29.3
2018 222 61.0 76.9 88.0 96.8 104 111 117 122 127 131 140 176 222
2019 103 28.3 35.7 40.8 45.0 48.4 51.5 54.2 56.6 58.9 61.0 64.8 81.7 103

M* – Missing or unknown data.

3 Methodology

In constructing the curves of IDF, the first step is to fit some theoretical frequency distribution to the maximum precipitation value of a group of certain periods [12]. Usually, local flood degradation data are not available with the accuracy required for cost-benefit analysis. The appropriate basis for decision-making is the threats and risks to the public safety of society. Risk assessment can be considered crucial to the selection of the recurrence period. In the current research, the maximum annual values of the available periods were statistically analyzed by three different methods of distribution, they are lognormal distribution, log Pearson III, and Gumbel distribution. The best fit was determined using EasyFit 5.5.

3.1 Gumbel distribution

In the theory of probability and statistics, distribution method of Gumbel (generalized extreme distribution value Type-I) can be applied for modeling the distribution of the highest value (or the lowest) for a set of samples of different distributions. The following equation gives the precipitation frequency P T (in mm) for every duration with a certain return period T R (in a year) [13].

(2) P T = P ave . + K T S ,

where P T represents the rainfall frequency (in mm) for each duration, S represents the standard deviation of precipitation data, P ave is the average of annual precipitation data, and K T is the Gumbel frequency factor given by equation (3).

(3) K T = 6 π { 0 . 5772 + ln ln T R T R 1 } ,

where, T R is the return period (2, 5, 10, 25, 50, and 100) years. Then, the rainfall intensity I T (in mm/h) for return period T R is obtained by equation (4).

(4) I T = P T T d ,

where T d represents the time duration in hour.

3.2 The log Pearson type III

It is a statistical method that can be used for fitting data of frequency distribution to predict the design flood of a stream at a specific location [14]. The merit of this procedure is that the extrapolation is made for the events values with return periods well behind the recorded events of flood. Equation (5) gives the simple expression for the aforementioned distribution.

(5) P T * = P ave . * + K T S ,

where, P ave . * is the average of P * values, P * is the logarithm of precipitation, S * is the standard deviation of P * values, and parameter K T is Pearson frequency factor which depends on return period (T R) and skewness coefficient (G). Values of K T factor can be obtained from tables in many hydrology references [28]. Skewness coefficient can be calculated by using equation (6).

(6) G = N ( P * P ave . * ) 3 ( N 1 ) ( N 2 ) S * ,

where N is the sample size (number of years of record).

3.3 Lognormal method

It follows the same steps of LPT III (i.e., logarithm values of the statistical variables) but with normal K T that was used in the method of Gumbel [12].

4 Results and discussion

4.1 Gumbel distribution method

It determines the return period intervals of 2, 5, 10, 25, 50, and 100 years for every duration, it needs many calculations. For every duration with a certain return period T r (in a year), rainfall frequency P t (in mm) can be determined depending on equation (2). After that, the intensity of rainfall I T (in mm/h) for T r return period can be achieved by equation (4).

The results obtained in Table 2 revealed that the rainfall intensity reduces as storm duration increases. Furthermore, rainfall of a certain duration has a higher intensity when it has a high return period.

Table 2

Computed Gumbel frequency factor ( K t ), rainfall ( P T ) in (mm), and intensity (I T) (in mm/hour) (using Gumbel method)

T R (Year) K T P T (mm) I T (mm/h)
0.5 h 1 h 1.5 h 2 h 2.5 h 3 h 3.5 h 4 h 4.5 h 5 h
2 −0.16 12.3 24.6 12.3 8.2 6.15 4.92 4.10 3.52 3.08 2.73 2.46
5 0.72 17.8 35.5 17.9 11.9 8.89 7.11 5.92 5.08 4.44 3.95 3.55
10 1.3 21.4 42.8 21.7 14.3 11 8.55 7.12 6.11 5.34 4.75 4.27
25 2.04 26.0 51.9 26.4 17.3 13.0 10.4 8.66 7.42 6.49 5.77 5.19
50 2.59 29.4 58.8 30.0 19.6 14.7 11.8 9.79 8.40 7.35 6.53 5.88
100 3.14 32.8 65.6 33.5 21.9 16.4 13.1 10.9 9.37 8.20 7.29 6.56

Figure 4 shows the IDF curves for the Gumbel method. These curves are plotted on the normal scale for Gumbel methods (for T r = 2, 5, 10, 25, 50, and 100 years).

Figure 3 
                  Annual precipitation of Wasit Province (1992–2019) · Disaggregation of daily rainfall data into shorter durations
Figure 3

Annual precipitation of Wasit Province (1992–2019) · Disaggregation of daily rainfall data into shorter durations

4.2 LPT III distribution

The LPT III distribution model can be applied to determine the precipitation intensities for different return periods and precipitation durations from historical IDF curves. The LPT III distribution includes the logarithms of the computed values. The precipitation frequency was calculated by the LPT III procedure using equation (5), as shown in Table 3.

Table 3

Statistical variables calculated using LPT III

Year P P * = Log P (P * P ave )2 (P* − P ave )3
1992 10.2 1.01 0.01 0.00
1993 15 1.18 0.01 0.00
1994 16.2 1.21 0.01 0.00
1995 9.1 0.96 0.02 0.00
1996 18.3 1.26 0.03 0.01
1997 15.3 1.18 0.01 0.00
1998 9.6 0.98 0.01 0.00
1999 10.8 1.03 0.00 0.00
2000 8 0.90 0.03 -0.01
2001 8.4 0.92 0.03 0.00
2002 11.2 1.05 0.00 0.00
M *
2011 9.1 0.96 0.02 0.00
2012 9.8 0.99 0.01 0.00
2013 15.4 1.19 0.01 0.00
2014 17.2 1.24 0.02 0.00
2015 13.9 1.14 0.00 0.00
2016 10.1 1.00 0.01 0.00
2017 4.8 0.68 0.17 -0.07
2018 33.4 1.52 0.19 0.08
2019 20.5 1.31 0.05 0.01
Ave. 13.3 1.09 ⅀ 0.64 ⅀ 0.02

S* = 0.18 G* = 0.2. P T (in mm) for every duration of precipitation applied in LPT III method.

Table 4 shows the computed values of K T for T r = 2, 5, 10, 25, 50, and 100 years that lead to calculate the frequency rainfall depth (Table 5).

Table 4

Calculated Pearson frequency factor ( K T ) and precipitation ( P T ) in (mm) using LPT distribution method

T r 2 5 10 25 50 100
K T −0.033 0.83 1.301 1.818 2.159 2.472
P T 1.08 1.24 1.32 1.42 1.48 1.53
Table 5

Computed intensity ( I T ) in (mm/h) (LPT III method)

Duration (h) Intensity (mm/h)
2 years 5 years 10 years 25 years 50 years 100 years
0.5 25.18 34.76 41.78 52.60 60.40 67.76
1 12.59 17.38 20.89 26.30 30.20 33.88
1.5 8.39 11.59 13.93 17.53 20.13 22.59
2 6.30 8.69 10.45 13.15 15.10 16.94
2.5 5.04 6.95 8.36 10.52 12.08 13.55
3 4.20 5.79 6.96 8.77 10.07 11.29
3.5 3.60 4.97 5.97 7.51 8.63 9.68
4 3.15 4.35 5.22 6.58 7.55 8.47
4.5 2.80 3.86 4.64 5.84 6.71 7.53
5 2.52 3.48 4.18 5.26 6.04 6.78

To estimate floods in rural/urban basins, the resulted curves of IDF can be used. Using the resulted curves of IDF is recommended for the safe, efficient, and rigorous design of flood protection projects and hydraulic structures. Thus, the obtained IDF curves can be used, as displayed in Figure 5.

Figure 4 
                  IDF curves for the study area (Gumbel method).
Figure 4

IDF curves for the study area (Gumbel method).

4.3 Lognormal method

It can be calculated by the IDF using Lognormal method for 30-min duration and return periods of 2, 5, 10, 25, 50, and 100 years, as shown in Table 6, and Figure 6 shows rainfall intensity IDF curves for lognormal method.

Table 6

Calculated intensity ( I T ) in (mm/h) (lognormal method)

P T I T
0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
12.59 25.18 12.59 8.39 6.30 5.04 4.20 3.60 3.15 2.80 2.52
16.6 33.20 16.60 11.07 8.30 6.64 5.53 4.74 4.15 3.69 3.32
20.89 41.78 20.89 13.93 10.45 8.36 6.96 5.97 5.22 4.64 4.18
28.84 57.68 28.84 19.23 14.42 11.54 9.61 8.24 7.21 6.41 5.77
36.31 72.62 36.31 24.21 18.16 14.52 12.10 10.37 9.08 8.07 7.26
45.71 91.42 45.71 30.47 22.86 18.28 15.24 13.06 11.43 10.16 9.14
Figure 5 
                  Rainfall intensity IDF curves of Wasit Province (LPT method).
Figure 5

Rainfall intensity IDF curves of Wasit Province (LPT method).

4.4 Goodness of fit test

For this study, LPT III, lognormal, and Gumbel distributions were applied for all series of data duration. Software of EasyFit 5.5 was used to fit the probability distributions for every rainfall duration data. For assessing the stability of every distribution of probability, the goodness of fit test, cumulative distribution graph, the least sum of statistic model identification criterion (LSSMIC), and probability density function (PDF) graph were applied. The probability distribution with lowest value of statistics showed higher fitting distribution according to the tests of fit goodness. Figures 710 show the PDFs, cumulative distribution functions (CDFs), probability difference, and probability–probability (P–P) plot of three selected distribution for 30 min.

Figure 6 
                  Rainfall intensity IDF curves of Wasit Province (lognormal method).
Figure 6

Rainfall intensity IDF curves of Wasit Province (lognormal method).

Figure 7 
                  PDFs of three selected distributions for 0.5 h.
Figure 7

PDFs of three selected distributions for 0.5 h.

Figure 8 
                  CDFs of three selected distributions for 0.5 h.
Figure 8

CDFs of three selected distributions for 0.5 h.

Figure 9 
                  Probability difference of three selected distributions for 0.5 h.
Figure 9

Probability difference of three selected distributions for 0.5 h.

Figure 7 represents the PDF of three selected distribution for 0.5 h.The PDF is the function that represents any probability distribution by integration. The PDF is always positive, and its integral from ∞− to ∞+ is equal to one. The PDF can be described as an evaluation of the histogram continuum that represents the relative frequencies within the ranges of the graphed results. The PDF value of the Gumbel distribution is less than 0.06, the lognormal distribution is about 0.08, and finally, the LPT III distribution is about 0.12 as shown in the figure.

As shown in Figure 8, the CDF is a function that gives the probability distribution of a random variable with its value being a real number. the CDF of three selected distributions for 0.5 h, where the CDF value for the three distributions was approximately between 0.9 and 1 as shown in the figure.

Figure 9 represents the plot of probability difference, which is a graph that shows the difference between the theoretical and empirical CDF values. It can be used for determining the degree of compatibility between the used theoretical distribution and the observed data, it also compares the fit goodness of various fitted distributions. It shows a scatterplot or a continuous curve for continuous distributions and a set of vertical lines for separated distributions (at every integer x).

Finally, Figure 10 shows the probability–probability (P–P) plot, which is a graph of the empirical CDF values plotted against the theoretical CDF values. It is used to determine how well a specific distribution fits the observed data. This plot will be approximately linear if the specified theoretical distribution is the correct model. EasyFit displays the reference diagonal line along which the graph points should fall.

The results of descriptive statistics of each duration series are displayed by EasyFit 5.5 program (Table 7).

Table 7

Results of descriptive statistics of rain data duration series

Statistic 0.5 h 1 h 6 h 12 h 24 h
Sample size 20 20 20 20 20
Range 53.21 67.05 121.83 153.5 193.4
Mean value 17.786 22.409 40.721 51.305 64.64
Variance 133.87 212.53 701.7 1113.9 1768.4
Std. deviation 11.57 14.578 26.49 33.376 42.052
Coef. of variation 0.650 0.650 0.650 0.650 0.650
Skewness 2.988 2.988 2.988 2.988 2.988
Figure 10 
                  P–P plot of three selected distributions for 0.5 h.
Figure 10

P–P plot of three selected distributions for 0.5 h.

It is observed that the skewness values of all the data durations are approximately the same, with a value of 2.988. This means that the distribution of each data duration is approximately symmetrical. So is the coefficient of variance which is same for all periods, indicating the correctness of the work of the program and the accuracy of the results, the coefficient of variance is 0.650 for all periods. The best fit for each distribution was assessed using the Chi-square, Anderson–Darling, and Kolmogorov–Smirnov fit test for each period using EasyFit 5.5. With the help of a quality fit test and LSSMIC, the best appropriate distribution has been determined. The results showed that the best distribution is lognormal method as shown in Table 8, where LSSMIC is calculated from

(7) LSSMIC = Abs ( 1 sum of statistic ) .

Table 8

Best fit model for 0.5 h

Distribution Kolmogorov–Smirnov Anderson–Darling Chi-square LSSMIC Best fit
Statistic Statistic Statistic
Gumbel 0.18802 1.3418 8.2854 8.81522
Lognormal 0.19454 0.59911 1.0353 0.82895 Best
LPT III 0.11574 0.51631 3.0515 2.68355

5 Conclusion and recommendations

5.1 Conclusion

  1. Produced new IDF curves should be utilized to estimate the rainfall intensities for varied return times. The revised IDF curve can be used to build and maintain urban water management systems such as culverts and bridges, among other things.

  2. According to the obtained results of the current study about the precipitation data of Wasit, it is observed that no significant difference can be found in the rainfall analysis results of the curves of IDF between the applied methods. This can be attributed to the fact that a semi-arid climate and flat terrain are dominant in Wasit that cause slight differences in rainfall values.

  3. Easy Fit 5.5 software was applied for determining the best results distribution, and one can notice that the lognormal was the best distribution, as the LSSMIC statistical model was used for assessing the fit of each probability distribution. The results showed that the lowest value of LSSMIC, which was 0.82895, was with lognormal distribution, and it was adopted as the best distribution for the data of Kut city.

  4. It was also inferred that the maximum intensity occurred in the return period of 100 years and the minimum intensity occurred in the 2 year return period.

5.2 Recommendations

The new IDF curves developed should be used in estimating rainfall intensities for various return periods, and the derived IDF models could be used for better results and accuracy. The new IDF curve can be used to design drainage systems and maintain urban water management systems such as culverts, drains, sewers, bridges, etc. The existing IDF curve should no longer be used because it was developed using poor data records and developed for a long time. Disaggregated rainfall data of Kut City stations can be used for hydrological analysis. It is strongly recommended that the new IDF curves should be reviewed or updated every 4–5 years because of climate change and variability patterns of rainfall data.

  1. Conflict of interest: The authors declare that they have no conflict of interest.

  2. Data availability statement: Most datasets generated and analyzed in this study are comprised in this submitted manuscript. The other datasets are available on reasonable request from the corresponding author with the attached information.

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Received: 2022-03-06
Revised: 2022-04-27
Accepted: 2022-05-10
Published Online: 2022-12-05

© 2022 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  3. Key competences for Transport 4.0 – Educators’ and Practitioners’ opinions
  4. COVID-19 lockdown impact on CERN seismic station ambient noise levels
  5. Constraint evaluation and effects on selected fracture parameters for single-edge notched beam under four-point bending
  6. Minimizing form errors in additive manufacturing with part build orientation: An optimization method for continuous solution spaces
  7. The method of selecting adaptive devices for the needs of drivers with disabilities
  8. Control logic algorithm to create gaps for mixed traffic: A comprehensive evaluation
  9. Numerical prediction of cavitation phenomena on marine vessel: Effect of the water environment profile on the propulsion performance
  10. Boundary element analysis of rotating functionally graded anisotropic fiber-reinforced magneto-thermoelastic composites
  11. Effect of heat-treatment processes and high temperature variation of acid-chloride media on the corrosion resistance of B265 (Ti–6Al–4V) titanium alloy in acid-chloride solution
  12. Influence of selected physical parameters on vibroinsulation of base-exited vibratory conveyors
  13. System and eco-material design based on slow-release ferrate(vi) combined with ultrasound for ballast water treatment
  14. Experimental investigations on transmission of whole body vibration to the wheelchair user's body
  15. Determination of accident scenarios via freely available accident databases
  16. Elastic–plastic analysis of the plane strain under combined thermal and pressure loads with a new technique in the finite element method
  17. Design and development of the application monitoring the use of server resources for server maintenance
  18. The LBC-3 lightweight encryption algorithm
  19. Impact of the COVID-19 pandemic on road traffic accident forecasting in Poland and Slovakia
  20. Development and implementation of disaster recovery plan in stock exchange industry in Indonesia
  21. Pre-determination of prediction of yield-line pattern of slabs using Voronoi diagrams
  22. Urban air mobility and flying cars: Overview, examples, prospects, drawbacks, and solutions
  23. Stadiums based on curvilinear geometry: Approximation of the ellipsoid offset surface
  24. Driftwood blocking sensitivity on sluice gate flow
  25. Solar PV power forecasting at Yarmouk University using machine learning techniques
  26. 3D FE modeling of cable-stayed bridge according to ICE code
  27. Review Articles
  28. Partial discharge calibrator of a cavity inside high-voltage insulator
  29. Health issues using 5G frequencies from an engineering perspective: Current review
  30. Modern structures of military logistic bridges
  31. Retraction
  32. Retraction note: COVID-19 lockdown impact on CERN seismic station ambient noise levels
  33. Special Issue: Trends in Logistics and Production for the 21st Century - Part II
  34. Solving transportation externalities, economic approaches, and their risks
  35. Demand forecast for parking spaces and parking areas in Olomouc
  36. Rescue of persons in traffic accidents on roads
  37. Special Issue: ICRTEEC - 2021 - Part II
  38. Switching transient analysis for low voltage distribution cable
  39. Frequency amelioration of an interconnected microgrid system
  40. Wireless power transfer topology analysis for inkjet-printed coil
  41. Analysis and control strategy of standalone PV system with various reference frames
  42. Special Issue: AESMT
  43. Study of emitted gases from incinerator of Al-Sadr hospital in Najaf city
  44. Experimentally investigating comparison between the behavior of fibrous concrete slabs with steel stiffeners and reinforced concrete slabs under dynamic–static loads
  45. ANN-based model to predict groundwater salinity: A case study of West Najaf–Kerbala region
  46. Future short-term estimation of flowrate of the Euphrates river catchment located in Al-Najaf Governorate, Iraq through using weather data and statistical downscaling model
  47. Utilization of ANN technique to estimate the discharge coefficient for trapezoidal weir-gate
  48. Experimental study to enhance the productivity of single-slope single-basin solar still
  49. An empirical formula development to predict suspended sediment load for Khour Al-Zubair port, South of Iraq
  50. A model for variation with time of flexiblepavement temperature
  51. Analytical and numerical investigation of free vibration for stepped beam with different materials
  52. Identifying the reasons for the prolongation of school construction projects in Najaf
  53. Spatial mixture modeling for analyzing a rainfall pattern: A case study in Ireland
  54. Flow parameters effect on water hammer stability in hydraulic system by using state-space method
  55. Experimental study of the behaviour and failure modes of tapered castellated steel beams
  56. Water hammer phenomenon in pumping stations: A stability investigation based on root locus
  57. Mechanical properties and freeze-thaw resistance of lightweight aggregate concrete using artificial clay aggregate
  58. Compatibility between delay functions and highway capacity manual on Iraqi highways
  59. The effect of expanded polystyrene beads (EPS) on the physical and mechanical properties of aerated concrete
  60. The effect of cutoff angle on the head pressure underneath dams constructed on soils having rectangular void
  61. An experimental study on vibration isolation by open and in-filled trenches
  62. Designing a 3D virtual test platform for evaluating prosthetic knee joint performance during the walking cycle
  63. Special Issue: AESMT-2 - Part I
  64. Optimization process of resistance spot welding for high-strength low-alloy steel using Taguchi method
  65. Cyclic performance of moment connections with reduced beam sections using different cut-flange profiles
  66. Time overruns in the construction projects in Iraq: Case study on investigating and analyzing the root causes
  67. Contribution of lift-to-drag ratio on power coefficient of HAWT blade for different cross-sections
  68. Geotechnical correlations of soil properties in Hilla City – Iraq
  69. Improve the performance of solar thermal collectors by varying the concentration and nanoparticles diameter of silicon dioxide
  70. Enhancement of evaporative cooling system in a green-house by geothermal energy
  71. Destructive and nondestructive tests formulation for concrete containing polyolefin fibers
  72. Quantify distribution of topsoil erodibility factor for watersheds that feed the Al-Shewicha trough – Iraq using GIS
  73. Seamless geospatial data methodology for topographic map: A case study on Baghdad
  74. Mechanical properties investigation of composite FGM fabricated from Al/Zn
  75. Causes of change orders in the cycle of construction project: A case study in Al-Najaf province
  76. Optimum hydraulic investigation of pipe aqueduct by MATLAB software and Newton–Raphson method
  77. Numerical analysis of high-strength reinforcing steel with conventional strength in reinforced concrete beams under monotonic loading
  78. Deriving rainfall intensity–duration–frequency (IDF) curves and testing the best distribution using EasyFit software 5.5 for Kut city, Iraq
  79. Designing of a dual-functional XOR block in QCA technology
  80. Producing low-cost self-consolidation concrete using sustainable material
  81. Performance of the anaerobic baffled reactor for primary treatment of rural domestic wastewater in Iraq
  82. Enhancement isolation antenna to multi-port for wireless communication
  83. A comparative study of different coagulants used in treatment of turbid water
  84. Field tests of grouted ground anchors in the sandy soil of Najaf, Iraq
  85. New methodology to reduce power by using smart street lighting system
  86. Optimization of the synergistic effect of micro silica and fly ash on the behavior of concrete using response surface method
  87. Ergodic capacity of correlated multiple-input–multiple-output channel with impact of transmitter impairments
  88. Numerical studies of the simultaneous development of forced convective laminar flow with heat transfer inside a microtube at a uniform temperature
  89. Enhancement of heat transfer from solar thermal collector using nanofluid
  90. Improvement of permeable asphalt pavement by adding crumb rubber waste
  91. Study the effect of adding zirconia particles to nickel–phosphorus electroless coatings as product innovation on stainless steel substrate
  92. Waste aggregate concrete properties using waste tiles as coarse aggregate and modified with PC superplasticizer
  93. CuO–Cu/water hybrid nonofluid potentials in impingement jet
  94. Satellite vibration effects on communication quality of OISN system
  95. Special Issue: Annual Engineering and Vocational Education Conference - Part III
  96. Mechanical and thermal properties of recycled high-density polyethylene/bamboo with different fiber loadings
  97. Special Issue: Advanced Energy Storage
  98. Cu-foil modification for anode-free lithium-ion battery from electronic cable waste
  99. Review of various sulfide electrolyte types for solid-state lithium-ion batteries
  100. Optimization type of filler on electrochemical and thermal properties of gel polymer electrolytes membranes for safety lithium-ion batteries
  101. Pr-doped BiFeO3 thin films growth on quartz using chemical solution deposition
  102. An environmentally friendly hydrometallurgy process for the recovery and reuse of metals from spent lithium-ion batteries, using organic acid
  103. Production of nickel-rich LiNi0.89Co0.08Al0.03O2 cathode material for high capacity NCA/graphite secondary battery fabrication
  104. Special Issue: Sustainable Materials Production and Processes
  105. Corrosion polarization and passivation behavior of selected stainless steel alloys and Ti6Al4V titanium in elevated temperature acid-chloride electrolytes
  106. Special Issue: Modern Scientific Problems in Civil Engineering - Part II
  107. The modelling of railway subgrade strengthening foundation on weak soils
  108. Special Issue: Automation in Finland 2021 - Part II
  109. Manufacturing operations as services by robots with skills
  110. Foundations and case studies on the scalable intelligence in AIoT domains
  111. Safety risk sources of autonomous mobile machines
  112. Special Issue: 49th KKBN - Part I
  113. Residual magnetic field as a source of information about steel wire rope technical condition
  114. Monitoring the boundary of an adhesive coating to a steel substrate with an ultrasonic Rayleigh wave
  115. Detection of early stage of ductile and fatigue damage presented in Inconel 718 alloy using instrumented indentation technique
  116. Identification and characterization of the grinding burns by eddy current method
  117. Special Issue: ICIMECE 2020 - Part II
  118. Selection of MR damper model suitable for SMC applied to semi-active suspension system by using similarity measures
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