scholarly journals Performance evaluation of Auto-Regressive Integrated Moving Average models for forecasting saltwater intrusion into Mekong river estuaries of Vietnam

2021 ◽  
Author(s):  
Thai Tran Thanh ◽  
Liem Nguyen Duy ◽  
Luu Pham Thanh ◽  
Yen Nguyen Thi My ◽  
Yen Tran Thi Hoang ◽  
...  
Keyword(s):  
Saltwater Intrusion ◽  
Moving Average ◽  
Mekong Delta ◽  
River System ◽  
Mekong River ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
Sowing Time ◽  
Crop Selection ◽  
Auto Regressive

The Mekong Delta is the most severely affected area by saltwater intrusion in Vietnam. Recent studies have focused on predicting this disaster with weekly and decade lead times without many seasonal forecasts, which is important for planning crop selection, crop structure, and sowing time. This study aims to forecast the spatial distribution of saltwater intrusion into the Mekong river estuaries of Vietnam during the dry season of 2021 by integrating Auto-Regressive Integrated Moving Average with Geographic Information System. ARIMA models were trained with a single input of water salinity measurements from 2012 to 2020. Compared to the weekly salinity observations in 2021, these models predicted very well in the My Tho and Ham Luong rivers but unsatisfactory performance in the Co Chien river. The deepest saltwater intrusion will occur between March 19th and April 16th of 2021, when the 4‰ saline front will move the farthest distance of 41,41 and 44 kilometers inland from the sea through My Tho, Ham Luong Co Chien rivers, respectively. The entire river system will be exposed to moderate risk of saltwater intrusion. Freshwater zones decreased significantly to 0.73% of the whole area of Ben Tre province. These findings could provide a valuable scientific foundation for the appropriate management of coastal aquifers to control or reduce saltwater intrusion.

scholarly journals Forecasting of saline intrusion in Ham Luong river, Ben Tre province (Southern Vietnam) using Box-Jenkins ARIMA models

2020 ◽  
Vol 23 (1) ◽  
pp. 446-453
Author(s):  
Thai Thanh Tran ◽  
Luong Duc Thien ◽  
Ngo Xuan Quang ◽  
Lam Van Tan
Keyword(s):  
Economic Development ◽  
Moving Average ◽  
Arima Model ◽  
Mekong River ◽  
Saline Intrusion ◽  
Arima Models ◽  
Southern Vietnam ◽  
Forecasting Methods ◽  
Auto Regressive ◽  
Local Residents

Introduction: Ham Luong River is a branch of Mekong River located in Ben Tre Province, which has played a crucial role in supporting livelihoods of local residents and the province's economic development. However, the saline intrusion has been expanding in Ham Luong River, which seriously affects the productive agriculture, aquaculture, and further causes tremendous difficulties for local people's lives. Thus, it is crucial to have research for forecast the saline intrusion in Ham Luong River. Our aim was to develop mathematical models in order to forecast the saline intrusion in Ham Luong River, Ben Tre Province. Methods: The Auto regressive integrated moving average (ARIMA) model was built to forecast the weekly saline intrusion in Ham Luong River, which has been obtained from Ben Tre Province's Hydro-Meteorological Forecasting Center over eight years (from 2012 to 2019). Results: The saline concentration increased from January to March and then decreased from April to June. The highest salinity occurred in February and March while the lowest salinity was observed in early June. Moreover, the ARIMA technique provided an adequate predictive model for a forecast of the saline intrusion in An Thuan, Son Doc, and An Hiep station. However, the ARIMA model in My Hoa and Vam Mon might be improved upon by other forecasting methods. Conclusion: Our study suggested that the nonseasonal/seasonal ARIMA is an easy-to-use modeling tool for a quick forecast of the saline intrusion.

scholarly journals Flooding in the Mekong Delta: Impact of dyke systems on downstream hydrodynamics

2019 ◽  
Author(s):  
Vo Quoc Thanh ◽  
Dano Roelvink ◽  
Mick van der Wegen ◽  
Johan Reyns ◽  
Herman Kernkamp ◽  
...  
Keyword(s):  
Mekong Delta ◽  
River System ◽  
Dry Season ◽  
Water Levels ◽  
Mekong River ◽  
Flood Season ◽  
Tidal Propagation ◽  
River Outflow ◽  
Year 2000

Abstract. Building high dykes is a common measure to cope with floods and plays an important role in agricultural management in the Vietnamese Mekong Delta. However, the construction of high dykes cause considerable changes in hydrodynamics of the Mekong River. Therefore, this paper aims to assess the impacts of the high dyke system on water level fluctuation and tidal propagation on the Mekong River branches using a modelling approach. In order to consider interaction between rivers and seas, an unstructured modelling grid was generated, with 1D–2D coupling, covering the Mekong Delta and extending to the East (South China Sea) and West (Gulf of Thailand) seas. The model was manually calibrated for the flood season of the year 2000. To assess the role of floodplains, scenarios consisting of high dykes built in different regions of the Long Xuyen Quadrangle (LXQ), Plains of Reeds (PoR) and TransBassac were carried out. Results show that the percentage of river outflow at Dinh An sharply increases in the dry season in comparison to the flood season while the other Mekong estuarine outflows rise slightly. In contrast, the lateral river flows of the Mekong River system to the seas by the Soai Rap mouth and the LXQ decrease somewhat in the dry season compared to the flood season due to overflow reduction at the Cambodia–Vietnam border. Additionally, the high dykes in the regions that are directly connected to a branch of the Mekong River, not only have an influence on the hydrodynamics in their own branch, but also on other branches because of the connecting channel of Vam Nao. Moreover, the high dykes built in the PoR, LXQ and TransBassac regions are the most important factor for changing water levels at Tan Chau, Chau Doc and Can Tho, respectively. The LXQ high dykes result in an increase of daily mean water levels and a decrease of tidal amplitudes on the Song Tien (downstream of the connecting channel of Vam Nao). A similar interaction is also found for the the PoR high dykes and the Song Hau.

Prognosticating the spread of covid-19 pandemic based on optimal arima estimators

2020 ◽  
Vol 20 ◽  
Author(s):  
Venuka Sandhir ◽  
Vinod Kumar ◽  
Vikash Kumar
Keyword(s):  
South Africa ◽  
Statistical Analysis ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Stable Case ◽  
Johns Hopkins University ◽  
Modelling Techniques ◽  
Auto Regressive ◽  
Global Threat

Background: COVID-19 cases have been reported as a global threat and several studies are being conducted using various modelling techniques to evaluate patterns of disease dispersion in the upcoming weeks. Here we propose a simple statistical model that could be used to predict the epidemiological extent of community spread of COVID-19from the explicit data based on optimal ARIMA model estimators. Methods: Raw data was retrieved on confirmed cases of COVID-19 from Johns Hopkins University (https://github.com/CSSEGISandData/COVID-19) and Auto-Regressive Integrated Moving Average (ARIMA) model was fitted based on cumulative daily figures of confirmed cases aggregated globally for ten major countries to predict their incidence trend. Statistical analysis was completed by using R 3.5.3 software. Results: The optimal ARIMA model having the lowest Akaike information criterion (AIC) value for US (0,2,0); Spain (1,2,0); France (0,2,1); Germany (3,2,2); Iran (1,2,1); China (0,2,1); Russia (3,2,1); India (2,2,2); Australia (1,2,0) and South Africa (0,2,2) imparted the nowcasting of trends for the upcoming weeks. These parameters are (p, d, q) where p refers to number of autoregressive terms, d refers to number of times the series has to be differenced before it becomes stationary, and q refers to number of moving average terms. Results obtained from ARIMA model showed significant decrease cases in Australia; stable case for China and rising cases has been observed in other countries. Conclusion: This study tried their best at predicting the possible proliferate of COVID-19, although spreading significantly depends upon the various control and measurement policy taken by each country.

scholarly journals The Impacts of Hydropower Dams in the Mekong River Basin: A Review

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 265
Author(s):  
Akarath Soukhaphon ◽  
Ian G. Baird ◽  
Zeb S. Hogan
Keyword(s):  
River Basin ◽  
River System ◽  
Economic Security ◽  
Mekong River ◽  
Aquatic Biodiversity ◽  
Environmental Impact Assessments ◽  
The Social ◽  
Hydropower Dams ◽  
River Hydrology ◽  
Riparian Communities

The Mekong River, well known for its aquatic biodiversity, is important to the social, physical, and economic health of millions living in China, Myanmar, Laos, Thailand, Cambodia, and Vietnam. This paper explores the social and environmental impacts of several Mekong basin hydropower dams and groupings of dams and the geographies of their impacts. Specifically, we examined the 3S (Sesan, Sekong Srepok) river system in northeastern Cambodia, the Central Highlands of Vietnam, and southern Laos; the Khone Falls area in southern Laos; the lower Mun River Basin in northeastern Thailand; and the upper Mekong River in Yunnan Province, China, northeastern Myanmar, northern Laos, and northern Thailand. Evidence shows that these dams and groupings of dams are affecting fish migrations, river hydrology, and sediment transfers. Such changes are negatively impacting riparian communities up to 1000 km away. Because many communities depend on the river and its resources for their food and livelihood, changes to the river have impacted, and will continue to negatively impact, food and economic security. While social and environmental impact assessments have been carried out for these projects, greater consideration of the scale and cumulative impacts of dams is necessary.

scholarly journals Saltwater intrusion into groundwater systems in the Mekong Delta and links to global change

2021 ◽  
Author(s):  
Han Xiao ◽  
Yin Tang ◽  
Hai-Ming Li ◽  
Lu Zhang ◽  
Thanh Ngo-Duc ◽  
...  
Keyword(s):  
Global Change ◽  
Saltwater Intrusion ◽  
Mekong Delta ◽  
Groundwater Systems

scholarly journals Seasonal Rainfall Forecasts for the Yangtze River Basin in the Extreme Summer of 2020

2021 ◽  
Author(s):  
Philip E. Bett ◽  
Gill M. Martin ◽  
Nick Dunstone ◽  
Adam A. Scaife ◽  
Hazel E. Thornton ◽  
...  
Keyword(s):  
Linear Regression ◽  
River Basin ◽  
Yangtze River ◽  
Asian Summer Monsoon ◽  
Yangtze River Basin ◽  
Early Summer ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
June July ◽  
The Impact

AbstractSeasonal forecasts for Yangtze River basin rainfall in June, May–June–July (MJJ), and June–July–August (JJA) 2020 are presented, based on the Met Office GloSea5 system. The three-month forecasts are based on dynamical predictions of an East Asian Summer Monsoon (EASM) index, which is transformed into regional-mean rainfall through linear regression. The June rainfall forecasts for the middle/lower Yangtze River basin are based on linear regression of precipitation. The forecasts verify well in terms of giving strong, consistent predictions of above-average rainfall at lead times of at least three months. However, the Yangtze region was subject to exceptionally heavy rainfall throughout the summer period, leading to observed values that lie outside the 95% prediction intervals of the three-month forecasts. The forecasts presented here are consistent with other studies of the 2020 EASM rainfall, whereby the enhanced mei-yu front in early summer is skillfully forecast, but the impact of midlatitude drivers enhancing the rainfall in later summer is not captured. This case study demonstrates both the utility of probabilistic seasonal forecasts for the Yangtze region and the potential limitations in anticipating complex extreme events driven by a combination of coincident factors.

scholarly journals Attribution of Long-Term Evapotranspiration Trends in the Mekong River Basin with a Remote Sensing-Based Process Model

2021 ◽  
Vol 13 (2) ◽  
pp. 303
Author(s):  
Shi Hu ◽  
Xingguo Mo
Keyword(s):  
Remote Sensing ◽  
Water Resources ◽  
Solar Radiation ◽  
River Basin ◽  
Mekong Delta ◽  
Mekong River ◽  
Catchment Management ◽  
Mekong River Basin ◽  
Area Index ◽  
Available Water

Using the Global Land Surface Satellite (GLASS) leaf area index (LAI), the actual evapotranspiration (ETa) and available water resources in the Mekong River Basin were estimated with the Remote Sensing-Based Vegetation Interface Processes Model (VIP-RS). The relative contributions of climate variables and vegetation greening to ETa were estimated with numerical experiments. The results show that the average ETa in the entire basin increased at a rate of 1.16 mm year−2 from 1980 to 2012 (36.7% of the area met the 95% significance level). Vegetation greening contributed 54.1% of the annual ETa trend, slightly higher than that of climate change. The contributions of air temperature, precipitation and the LAI were positive, whereas contributions of solar radiation and vapor pressure were negative. The effects of water supply and energy availability were equivalent on the variation of ETa throughout most of the basin, except the upper reach and downstream Mekong Delta. In the upper reach, climate warming played a critical role in the ETa variability, while the warming effect was offset by reduced solar radiation in the Mekong Delta (an energy-limited region). For the entire basin, the available water resources showed an increasing trend due to intensified precipitation; however, in downstream areas, additional pressure on available water resources is exerted due to cropland expansion with enhanced agricultural water consumption. The results provide scientific basis for practices of integrated catchment management and water resources allocation.

Towards optimal ATM cash replenishment using time series analysis

2021 ◽  
pp. 1-13
Author(s):  
Muhammad Rafi ◽  
Mohammad Taha Wahab ◽  
Muhammad Bilal Khan ◽  
Hani Raza
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Automatic Teller Machine ◽  
Proposed Model ◽  
Auto Regressive ◽  
Specific Amount

Automatic Teller Machine (ATM) are still largely used to dispense cash to the customers. ATM cash replenishment is a process of refilling ATM machine with a specific amount of cash. Due to vacillating users demands and seasonal patterns, it is a very challenging problem for the financial institutions to keep the optimal amount of cash for each ATM. In this paper, we present a time series model based on Auto Regressive Integrated Moving Average (ARIMA) technique called Time Series ARIMA Model for ATM (TASM4ATM). This study used ATM back-end refilling historical data from 6 different financial organizations in Pakistan. There are 2040 distinct ATMs and 18 month of replenishment data from these ATMs are used to train the proposed model. The model is compared with the state-of- the-art models like Recurrent Neural Network (RNN) and Amazon’s DeepAR model. Two approaches are used for forecasting (i) Single ATM and (ii) clusters of ATMs (In which ATMs are clustered with similar cash-demands). The Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE) are used to evaluate the models. The suggested model produces far better forecasting as compared to the models in comparison and produced an average of 7.86/7.99 values for MAPE/SMAPE errors on individual ATMs and average of 6.57/6.64 values for MAPE/SMAPE errors on clusters of ATMs.

Modeling Time Series for Prediction of Thalassemia in Nineveh Governorate

2021 ◽  
Vol 2 (3) ◽  
pp. 120-131
Author(s):  
Shaymaa Riyadh Thanoon
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Monthly Data ◽  
Number Of Patients ◽  
Auto Regressive

The aim of this research is to analyze the time series of Thalassemia cancer cases by making assumptions on the number of cases to formulate the problem to find the best model for predicting the number of patients in Nineveh governorate using (Box and Jenkins) method of analysis based on the monthly data provided by Al Salam Hospital in Nineveh for the period (2014-2018). The results of the analysis showed that the appropriate model of analysis is the Auto-Regressive Integrated Moving Average (ARIMA) (2,1,0) and based on this model the number of people with this disease was predicted for the next two years where the results showed values ​​consistent with the original values which indicates the good quality of the model.

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