Modeling water quality and hydrological variables using ARIMA: a case study of Johor River, Malaysia

Katimon, Ayob; Shahid, Shamsuddin; Mohsenipour, Morteza

doi:10.1007/s40899-017-0202-8

Cited by 53 publications

(22 citation statements)

References 19 publications

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“…The ARIMA model utilizes correlation and trends in the historical time series data for forecasting and has been widely used for streamflow forecasting due to easy development and implementation. The ARIMA model has been successfully applied in multiple hydrologic modeling applications, including predicting the streamflow [19,20], rainfall [21,22], and groundwater [19,23]. The ARIMA model and its derivatives, such as seasonal ARIMA (SARIMA), periodic ARIMA, and ARMAX, are applied extensively in streamflow forecasting, particularly in the modeling of monthly streamflow [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Exploring Spatiotemporal Relations between Soil Moisture, Precipitation, and Streamflow for a Large Set of Watersheds Using Google Earth Engine

Sazib

Bolten

Mladenova

2020

Water

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An understanding of streamflow variability and its response to changes in climate conditions is essential for water resource planning and management practices that will help to mitigate the impacts of extreme events such as floods and droughts on agriculture and other human activities. This study investigated the relationship between precipitation, soil moisture, and streamflow over a wide range of watersheds across the United States using Google Earth Engine (GEE). The correlation analyses disclosed a strong association between precipitation, soil moisture, and streamflow, however, soil moisture was found to have a higher correlation with the streamflow relative to precipitation. Results indicated different strength of the association depends on the watershed classes and lag times assessments. The perennial watersheds showed higher coherence compared to intermittent watersheds. Previous month precipitation and soil moisture have a stronger influence on the current month streamflow, particularly in the snow-dominated watersheds. Monthly streamflow forecasting models were developed using an autoregressive integrated moving average (ARIMA) and support vector machine (SVM). The results showed that the SVM model generally performed better than the ARIMA model. Overall streamflow forecasting model performance varied considerably among watershed classes, and perennial watersheds tend to exhibit better predictably compared to intermittent watersheds due to lower streamflow variability. The SVM models with precipitation and streamflow inputs performed better than those with streamflow input only. Results indicated that the inclusion of antecedent root-zone soil moisture improved the streamflow forecasting in most of the watersheds, and the largest improvements occurred in the intermittent watersheds. In conclusion, this work demonstrated that knowing the relationship between precipitation, soil moisture, and streamflow in different watershed classes will enhance the understanding of the hydrologic process and can be effectively utilized in improving streamflow forecasting for better satellite-based water resource management strategies.

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Section: Introductionmentioning

confidence: 99%

Exploring Spatiotemporal Relations between Soil Moisture, Precipitation, and Streamflow for a Large Set of Watersheds Using Google Earth Engine

Sazib

Bolten

Mladenova

2020

Water

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“…Reliable assessment of flood frequency and magnitude is essential for effective mitigation planning and designing hydraulic structures [1]. Univariate analysis based on flood peak is generally used for predicting flood occurrence [2]. However, devastating flood does not always depend on peak flow.…”

Section: Introductionmentioning

confidence: 99%

Flood susceptibility assessment in Kelantan river basin using copula

Nashwan

Ismail

Ahmed

2018

IJET

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Bivariate frequency analysis of flood variables of different station locations of Kelantan river basin was conducted using copula for the assessment of the geographical distribution of flood risk. Seven univariate distribution functions of flood variables were fitted with flood variables such as peak flow, flood volume, and flood duration to find the best-fitted distributions. The joint dependent structures of flood variables were modeled using Gumbel copula. The results of the study revealed that different variables fit with different distributions. The correlation analysis among variables showed a strong association. Joint distribution functions of peak-flow and volume, peak-flow and duration, and volume and duration revealed that the joint return periods were much higher than univariate return periods of same flood variables. The flood risk analysis based on joint return period of flood variables revealed the highest risk of devastating flood in the downstream. The locations identified as highly susceptible to flood risk by joint distributing of flood variables had experienced most severe floods in recent history, which indicates the effectiveness of the method for the analysis of flood risk. It is expected that this procedure can be helpful for better assessment of flood impacts.

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“…Another crucial phase in the ARIMA model building is the residual diagnostics which were obtained by using MATLAB ® software. Literally, an ARIMA model is adequate when its residuals are normally distributed and random [56]. The standardized residual and residual histogram between the predicted and true values of the air dose rate of MP1 were calculated to test the model goodness.…”

Section: Tablementioning

confidence: 99%

A mobile simulation and ARIMA modeling for prediction of air radiation dose rates

Salh

Külahçı

Aközcan

2021

J Radioanal Nucl Chem

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A spatial simulation method in .mp4 format was proposed to determine Fukushima radioactive fallout transport and the Absorbed Dose Rate, Annual Effective Dose Equivalent, and Excess Lifetime Cancer Risk were determined for 10 months after the accident (March 11 2011). The findings of this study demonstrate that an appropriate ARIMA model can be applied for radiation dose time-series in the case of nuclear reactor accidents like Chernobyl and Fukushima to predict the future air dose rates, which can provide valuable information in determining the evacuation zones, decontamination processes, and radiation protection progresses. The model forecasted results and the actual observation data in the same period shows a gradual decrease in the air dose rates during the prediction period. Moreover, there is a good agreement between them as the prediction and observation scatter plot follows each other with small variations. These results provide important insights into the predictability of ARIMA models; thus, the models were utilized to forecast the air dose rates for the period (January 2020 -October 2020).

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Modeling water quality and hydrological variables using ARIMA: a case study of Johor River, Malaysia

Cited by 53 publications

References 19 publications

Exploring Spatiotemporal Relations between Soil Moisture, Precipitation, and Streamflow for a Large Set of Watersheds Using Google Earth Engine

Exploring Spatiotemporal Relations between Soil Moisture, Precipitation, and Streamflow for a Large Set of Watersheds Using Google Earth Engine

Flood susceptibility assessment in Kelantan river basin using copula

A mobile simulation and ARIMA modeling for prediction of air radiation dose rates

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