Trend analysis and ARIMA modelling of recent groundwater levels in the White Volta River basin of Ghana

Gibrilla, Abass; Anornu, Geophrey K.; Adomako, Dickson

doi:10.1016/j.gsd.2017.12.006

Cited by 58 publications

(18 citation statements)

References 28 publications

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“…Trend analysis of groundwater levels is an essential component of groundwater management, since it provides significant information about the direction and characteristics of the groundwater level trend, allowable discharge limit and the cause of groundwater decline ( [1]; [2]). Several studies (e.g.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Trend Analysis of Groundwater Levels in Sharjah, UAE

Yilmaz¹,

Shanableh²,

Al‐Ruzouq³

et al. 2020

IJESD

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United Arab Emirates (UAE) is a water stressed country due to limited rainfall. Moreover, rapid population growth, increase in agricultural and industrial activities are causing additional stress to the water resources in UAE. Groundwater is the most important fresh water source in UAE, and sustainable and efficient management of groundwater is vital for the country. Trend analysis of groundwater levels is an essential component of groundwater management, since it provides significant information about the direction and characteristics of the groundwater level trend, allowable discharge limit and the cause of groundwater decline. In this study, monthly groundwater level trends from eleven well fields were investigated over the period of 15 years in Sharjah, UAE. Groundwater spatiotemporal data analysis tool (GWSDAT) was used for the spatial and temporal trend analysis for groundwater levels. Mann-Kendall non-parametric test was used for the temporal trend analysis. Statistically significant decreasing trends were identified almost in all well fields in Sharjah. In parallel to decreasing groundwater levels, the groundwater production has fallen substantially (more than 90% decrease in some well fields over 15 years). It was found that the well fields close to the coastline experienced significant decrease in groundwater levels, which induces sea water intrusion and salinization of groundwater.

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Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Trend Analysis of Groundwater Levels in Sharjah, UAE

Yilmaz¹,

Shanableh²,

Al‐Ruzouq³

et al. 2020

IJESD

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“…In modeling for prediction using time series, solar radiation data use behavior of previous data. Representing mostly model with a linear concept of Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) model which traditional approach [9] [12]. The ARIMA model assumed that current data is a linear function of the previous data and error calculated also requires balance before it is used in the linear equation [8].…”

Section: B Autoregressive Integrated Moving Average (Arima) Modelmentioning

confidence: 99%

Multilayer Perceptron (MLP) and Autoregressive Integrated Moving Average (ARIMA) Models in Multivariate Input Time Series Data: Solar Irradiance Forecasting

Munandar

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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Solar irradiance needs to estimate power consumptions for requiring of saving energy. The demand accomplished by providing facilities to predict. Time series data is a dataset that has complex problems. Multilayer perceptron (MLP) and autoregressive integrated moving average (ARIMA) with multivariate input were used to solve the problem for predicting solar irradiance. The dataset was collected from solar irradiance sensor by an online monitoring station with 10 minutes data interval for 18 months. Prediction experimented with t, t-2, and t-6 data inputs that represent t as the day to get the predictive model (t+1). In ARIMA model, optimization was obtained in the input parameter (t-6), and ARIMA(1,1,2) with minimum RMSE is 43.91 W/m2, whereas MLP model used a single layer, ten neurons and using relu activation function to predict with minimum RMSE is 8.68 W/m2 using (t) input parameter. The deep learning model is better than the statistical model in this experiment. RMSE, MSE, MAE, MAPE, and R 2 , are used as an evaluation for model performance.

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“…In recent years, scholars have directed many research efforts toward the accurate prediction of water quality parameters. With the development of computer intelligence, many new forecasting methods have emerged, mainly including gray theory methods [1], [2], Markov modeling methods [3], support vector regression (SVR) [4], [5], the differential autoregressive integrated moving average (ARIMA) method [6], [7], neural network methods [8], [9], and long short-term memory (LSTM) [10], [11]. The fundamental data prediction principle of gray theory is to find the relevant information from the existing data that has the greatest impact on the prediction results and then predict future data; however, the prediction results for dynamic data are not ideal.…”

Section: Introductionmentioning

confidence: 99%

Radial Basis Function Neural Network Model for Dissolved Oxygen Concentration Prediction Based on an Enhanced Clustering Algorithm and Adam

Sun

Feng

2021

IEEE Access

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In fishery aquaculture, water quality directly determines the economic benefits of aquatic products, and dissolved oxygen is an important factor affecting water quality. To accurately grasp the trends of variation in dissolved oxygen, a dissolved oxygen concentration forecasting model based on an enhanced clustering algorithm and Adam with a radial basis function neural network (ECA-Adam-RBFNN) is proposed. An enhanced clustering algorithm (ECA) combining K-means with ant colony optimization is introduced in place of random selection to determine the center positions of the neural network hidden layer units. If the number of center points is too high, the neural network will be overfit, whereas if it is too low, sudden changes will appear in the results. Once the hidden layer centers have been determined, the radial basis function (RBF) width is calculated from the maximum center distance and the number of center points to avoid the two extreme cases of RBF that are too peaked or flat. The recursive least squares (RLS) algorithm is introduced to obtain the connection weights from the hidden layer to the output layer. The Adam algorithm is introduced to iteratively differentiate the objective function to adjust the center values, weights and width while adaptively varying the learning rates for these three types of parameters. Finally, the improved forecasting algorithm is applied for the prediction of the dissolved oxygen concentration in fishery aquaculture. The experimental results show that under identical conditions, compared with a long short-term memory (LSTM) network, a backpropagation neural network (BPNN), a traditional RBF neural network, a support vector regression (SVR) model, an autoregressive integrated moving average (ARIMA), K-MLPNN (K-means muhilayer perceptron neural networks), and SC-K-means-RBF model, the improved algorithm achieves significant reductions in the mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE) as model evaluation indicators.

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Trend analysis and ARIMA modelling of recent groundwater levels in the White Volta River basin of Ghana

Cited by 58 publications

References 28 publications

Spatio-Temporal Trend Analysis of Groundwater Levels in Sharjah, UAE

Spatio-Temporal Trend Analysis of Groundwater Levels in Sharjah, UAE

Multilayer Perceptron (MLP) and Autoregressive Integrated Moving Average (ARIMA) Models in Multivariate Input Time Series Data: Solar Irradiance Forecasting

Radial Basis Function Neural Network Model for Dissolved Oxygen Concentration Prediction Based on an Enhanced Clustering Algorithm and Adam

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