Trend analysis of major hydroclimatic variables in the Langat River basin, Malaysia

Ebrahimian, Mahboubeh; Nuruddin, Ahmad Ainuddin; Soom, Mohd Amin Mohd; Sood, Alias Mohd; Neng, Liew Ju; Galavi, Hadi

doi:10.1111/sjtg.12234

Cited by 21 publications

(8 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The basin comprised an area of approximately 2,394.38 km2, with a major river channel stretching for around 141 km. The river runs southward towards the lower mainland and westwards towards the coast of the Selangor state, with its mouth is located in the Straits of Malacca [24]. This river basin, Malaysia's most urbanised river basin, is considered to compensate for the advantages of Klang Valley spill-over development [25,26].…”

Section: Area Of Studymentioning

confidence: 99%

A Comparison of Multiple Imputation Methods for Recovering Missing Data in Hydrological Studies

Hamzah

Samad

2021

Civ Eng J

View full text Add to dashboard Cite

Missing data is a common problem in hydrological studies; therefore, data reconstruction is critical, especially when it is crucial to employ all available resources, even incomplete records. Furthermore, missing data could have an impact on statistical analysis results, and the amount of variability in the data would not be fittingly anticipated. As a result, this study compared the performance of three imputation methods in predicting recurrence in streamflow datasets: robust random regression imputation (RRRI), k-nearest neighbours (k-NN), and classification and regression tree (CART). Furthermore, entire historical daily streamflow data from 2012 to 2014 (as training dataset) were utilised to assess and validate the effectiveness of the imputation methods in addressing missing streamflow data. Following that, all three methods coupled with multiple linear regression (MLR), were used to restore streamflow rates in Malaysia's Langat River Basin from 1978 to 2016. The estimation techniques effectiveness was evaluated using metrics inclusive of the Nash-Sutcliffe efficiency coefficient (CE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE). The results confirmed that RRRI coupled with MLR (RRRI-MLR) had the lowest RMSE and MAPE values, outperforming all other techniques tested for filling missing data in daily streamflow datasets. This indicates that the RRRI-MLR is the best method for dealing with missing data in streamflow datasets. Doi: 10.28991/cej-2021-03091747 Full Text: PDF

show abstract

Section: Area Of Studymentioning

confidence: 99%

A Comparison of Multiple Imputation Methods for Recovering Missing Data in Hydrological Studies

Hamzah

Samad

2021

Civ Eng J

View full text Add to dashboard Cite

show abstract

“…The Langat River basin has a hot and humid tropical climate with a 27˚C average annual temperature, which is uniform throughout the year and a 2470 mm average annual rainfall distributed throughout the year [46].…”

Section: Study Areamentioning

confidence: 99%

Optimised neural network model for river-nitrogen prediction utilizing a new training approach

et al. 2020

View full text Add to dashboard Cite

In the past few decades, there has been a rapid growth in the concentration of nitrogenous compounds such as nitrate-nitrogen and ammonia-nitrogen in rivers, primarily due to increasing agricultural and industrial activities. These nitrogenous compounds are mainly responsible for eutrophication when present in river water, and for 'blue baby syndrome' when present in drinking water. High concentrations of these compounds in rivers may eventually lead to the closure of treatment plants. This study presents a training and a selection approach to develop an optimum artificial neural network model for predicting monthly average nitrate-N and monthly average ammonia-N. Several studies have predicted these compounds, but most of the proposed procedures do not involve testing various model architectures in order to achieve the optimum predicting model. Additionally, none of the models have been trained for hydrological conditions such as the case of Malaysia. This study presents models trained on the hydrological data from 1981 to 2017 for the Langat River in Selangor, Malaysia. The model architectures used for training are General Regression Neural Network (GRNN), Multilayer Neural Network and Radial Basis Function Neural Network (RBFNN). These models were trained for various combinations of internal parameters, input variables and model architectures. Post-training, the optimum performing model was selected based on the regression and error values and plot of predicted versus observed values. Optimum models provide promising results with a minimum overall regression value of 0.92.

show abstract

“…The analysis of the hydrological behavior over time often uses the Mann Kendall, and Pettitt's nonparametric methods, because of its simplicity and robustness (Ding, Wang & Lu, 2018;Ebrahimian et al, 2018;Xue et al, 2018;Zhang et al, 2018). The Pettitt test, besides verifying stationary in data series, also allows identifying the period in which the change occurred (Ebrahimian et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Nonparametric tests for stationary analysis in hydrological data

Aires

Reis

Campos

2019

J. Env. Anal. Progr.

View full text Add to dashboard Cite

One of the main problems for water resources management systems has been the climate change and the intensification of anthropogenic activities in river basins. In this context, this work aimed to analyze the dynamics of land use and cover and its influence on temporal variability on streamflow data. The behavior of hydrological data (streamflow and rainfall) over time was analyzed by applying the nonparametric tests of Mann Kendall and Pettitt. Images derived from orbital sensors using the Random Forest classifier assessed the anthropogenic influence in the area, land use, and cover classification. The rainfall data did not present significant changes over time, according to the applied tests. However, the low annual flow and average annual flow presented nonstationary behavior, with a trend of reduction over time. As rainfall did not change in its patterns over time, the main reason associated with the changes in streamflow regimes was associated with the changes in land use and land cover, especially in the areas for crops, that had an increase of 48% in the study period, which can contribute to increase the demand for water and affect the streamflow. The results obtained confirm the importance of this study for water management systems to adapt itself to the changes in hydrological behavior over time.

show abstract

Trend analysis of major hydroclimatic variables in the Langat River basin, Malaysia

Cited by 21 publications

References 38 publications

A Comparison of Multiple Imputation Methods for Recovering Missing Data in Hydrological Studies

A Comparison of Multiple Imputation Methods for Recovering Missing Data in Hydrological Studies

Optimised neural network model for river-nitrogen prediction utilizing a new training approach

Nonparametric tests for stationary analysis in hydrological data

Contact Info

Product

Resources

About