A new approach for suspended sediment load calculation based on generated flow discharge considering climate change

Adib, Arash; Kişi, Özgür; Khoramgah, Shekoofeh; Gafouri, H. R.; Liaghat, Ali; Lotfirad, Morteza; Moayyeri, Neda

doi:10.2166/ws.2021.069

Cited by 13 publications

(5 citation statements)

References 21 publications

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“…Iran lies in an arid and semi-arid climate far from moisture sources and also, rainy clouds have already lost a large portion of their moisture when they arrive in Iran, and they cannot induce rainfall in Central and Eastern Iran (Adib et al 2021). The distribution of precipitation is uneven in Iran.…”

Section: Introductionmentioning

confidence: 99%

Drought monitoring and prediction using SPI, SPEI, and random forest model in various climates of Iran

Lotfirad

Esmaeili-Gisavandani

Adib

2021

Journal of Water and Climate Change

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The aim of this study is to select the best model (combination of different lag times) for predicting the standardized precipitation index (SPI) and the standardized precipitation and evapotranspiration index (SPEI) in next time. Monthly precipitation and temperature data from 1960 to 2019 were used. In temperate climates, such as the north of Iran, the correlation coefficient of SPI and SPEI was 0.94, 0.95, and 0.81 at the time scales of 3, 12, and 48 months, respectively. Besides, this correlation coefficient was 0.47, 0.35, and 0.44 in arid and hot climates, such as the southwest of Iran because potential evapotranspiration (PET) depends on temperature more than rainfall. Drought was predicted using the random forest (RF) model and applying 1–12 months lag times for next time. By increasing of time scale, the prediction accuracy of SPI and SPEI will improve. The ability of SPEI is more than SPI for drought prediction, because the overall accuracy (OA) of prediction will increase, and the errors (i.e., overestimate (OE) and underestimate (UE)) will reduce. It is recommended for future studies (1) using wavelet analysis for improving accuracy of predictions and (2) using the Penman–Monteith method if ground-based data are available.

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Section: Introductionmentioning

confidence: 99%

Drought monitoring and prediction using SPI, SPEI, and random forest model in various climates of Iran

Lotfirad

Esmaeili-Gisavandani

Adib

2021

Journal of Water and Climate Change

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“…In Pettit (1979) research, a non-parametric approach has been developed for analyzing change points which are still used extensively. This test calculates transferring data average in different significance levels of hypothesis test (Liu et al 2012;Lotfirad et al 2018;Adib et al 2021). The Null hypothesis states that all data is homogenous, and there is an alternative hypothesis that opposes this hypothesis and indicates that there is one change in data average.…”

Section: Homogeneity Testmentioning

confidence: 99%

Detection of abrupt shift and non-parametric analyses of trends in runoff time series in the Dez river basin

Gholami¹,

Moradi²,

Lotfirad

et al. 2021

Water Supply

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The present study aims to investigate the homogeneity of runoff time series and also to review the existence of trends in Tale Zang hydrometric station (the hydrometric station in the entrance of Dez Reservoir) runoff, using 61 years (1956–2016) daily observatory data. Pettit test, which is a common method in investigating the homogeneity of time series, was used to identify change points. Both Mann-Kendall and auto-correlated Mann-Kendall tests were applied to analyze the existence or non-existence of trends in each annual, seasonal, and monthly time series observed in a runoff. In time series, significant trends in 95% level of confidence were recognized, upper and lower limit values were presented for Sen's slope and it was tested for the increasing or decreasing trends in nature. Based on the results of this study, the significant change point in 95% level of confidence was recognized in Annual, Spring, Summer, Autumn, March, May, June, July, August, September, and October in 1997,1997, 1999, 1997, 1999, 1999, 1999, 1997, 2000, 2007, 2008, respectively. Analyzing the existence of a trend in 95% level of confidence indicated that in Spring, and in March, September, and October, for both Mann-Kendall and auto-correlated Mann-Kendall tests, the trend is significant and additive until the change point.

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“…Other losses can hardly be considered monetary value. And we can point to the environmental changes that take place by floods, such as water pollution, erosion, and its social aspects (Adib, 2021). Moreover, Urbanization leads to land coming out its natural state.…”

Section: Introductionmentioning

confidence: 99%

Prediction of flood using optimized neural network with Gray wolf algorithm (Maroon River case study)

Doumari

2021

Present Environment and Sustainable Development

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Floods, as one of the most frequent natural hazards, cause irreparable damage to infrastructure and agriculture, and housing every year. In order to avoid financial and human losses, the importance of flood forecasting seems inevitable. Considering that floods are caused by many natural and anthropogenic factors and also have limitations such as lack of complete information. In this study, artificial neural networks have been used as an efficient method for flood prediction. The neural network inputs include the Dubai River and the Eshel River, this data was collected over 8 Years from the Maroon River. The network used is a multilayer perceptron, also the neural network weights are optimized by the Gray wolf algorithm and the results are compared with other common methods. Analysis of the output results shows that the neural network with the Gray Wolf algorithm has better results than neural network and Genetic algorithms and the error of this method is 0.53%, which indicates high accuracy and precision for flood prediction compared to other evolutionary algorithms. This method is used to obtain the best amount of data for testing and training. As the results, the best rate is 80% for training and 20% for testing. Obtained results show the neural network error squares with 80% of the training data and 20% of the test data.

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A new approach for suspended sediment load calculation based on generated flow discharge considering climate change

Cited by 13 publications

References 21 publications

Drought monitoring and prediction using SPI, SPEI, and random forest model in various climates of Iran

Drought monitoring and prediction using SPI, SPEI, and random forest model in various climates of Iran

Detection of abrupt shift and non-parametric analyses of trends in runoff time series in the Dez river basin

Prediction of flood using optimized neural network with Gray wolf algorithm (Maroon River case study)

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