Comparative Study of the Three Models (ANN-PMC),  (DWT-ANN-PMC) and (MLR) for Prediction of the Groundwater Level of the Surface Water Table in the Saïss Plain (North of Morocco)

Ibrahimi, Abdelhamid; Baali, Abdellatif; Amine, Couscous; Kamel, Touria El; Hamdani, Nadia

doi:10.22266/ijies2017.1031.24

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…Aussem et al [38] presented the formula l = int [log(n)] , where l is the decomposition level, n is the number of timeseries data, is the integer part function, and log denotes base-10 logarithms. Many recent studies have calculated the decomposition level using this formula [68][69][70]. In our study, the values for Ispir, Mescitli, and Laleli stations are 588, 564, and 492, respectively.…”

Section: Feed-forward Neural Network (Ffnn)mentioning

confidence: 89%

Predicting Monthly Streamflow Using a Hybrid Wavelet Neural Network: Case Study of the Çoruh River Basin

Güneş¹,

Parim²,

Yildiz³

et al. 2021

Pol. J. Environ. Stud.

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In this study, a hybrid model combining discrete wavelet transforms (WTs) and artificial neural networks (ANNs) is used to estimate the monthly streamflow. The WT-ANN hybrid model was developed using the Daubechies main wavelet to predict the streamflow for three gauging stations on the Çoruh river basin one month in advance, with different combinations of air temperature, precipitation, and streamflow variables, and their wavelet transformations. Four different hybrid WT-ANN models were generated and compared with four different conventional ANN models. The dataset was chronologically divided into training, validation, and testing data. The results indicated that the WT-ANN hybrid models performed better than the traditional ANN models for all three stations. Furthermore, the chronologically divided dataset was used to examine the effects of changes in hydrological data over time on model performance. In conclusion, model performances in the training period deteriorated during the validation and testing periods due to structural changes in the hydrological data.

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Section: Feed-forward Neural Network (Ffnn)mentioning

confidence: 89%

Predicting Monthly Streamflow Using a Hybrid Wavelet Neural Network: Case Study of the Çoruh River Basin

Güneş¹,

Parim²,

Yildiz³

et al. 2021

Pol. J. Environ. Stud.

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“…The discrete wavelet transform allowed most "noisy" data to be eliminated and facilitates the extraction of quasi-periodic and periodic signals in the original data time series [20]. In general, WA-ANFIS models had the best forecasting results in terms of R2 and RMSE for both stations.…”

Section: Discussionmentioning

confidence: 99%

Application of Several Artificial Intelligence Models for Forecasting Meteorological Drought Using the Standardized Precipitation Index in the Saïss Plain (Northern Morocco)

Ibrahimi¹,

Baali²

2018

IJIES

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Drought is one of the main natural factors influencing different aspects of human life. Over the decades, intelligent techniques have proven to be very capable of modeling and predicting nonlinear and dynamic time series. Therefore, the present study aims to predict drought by using and comparing neuro-fuzzy adaptive inference systems (ANFIS), artificial neural network of multilayered perceptron (ANN-MLP) and the support vector model (SVR). For this purpose, the precipitation data obtained from the Ain Bittit station were used for a statistical period of 34 years. In addition, the short-term (3 and 6 months) and long-term (9 and 12 months) time scales were calculated using the standardized precipitation index (SPI). Then, depending on the results of the calculations, the period 1979-2000 was selected as a control group and the period 2003-2012 was selected as an experimental group. In order to predict the SPI for the (t + 1) period, SPI values, precipitation from previous months were used. The results indicated that, in the majority of time scales, the ANFIS model gives SPI values and predictive dryness more accurately than the SVR, and ANN models.

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“…Based on the values of a collection of explanatory factors, LR is a useful model for determining whether an outcome will occur or not (Sahour et al, 2022). Multiple Linear Regression MLR is a regression algorithm used to model the relationship between multiple input variables and a continuous target variable (Ibrahimi et al, 2017). It assumes a linear relationship between the inputs and the target and estimates the coefficients that minimize the sum of squared errors.…”

Section: Regressionmentioning

confidence: 99%

“…This is explained by the fact that ANNs, like NNAR, learn from the patterns in the training data. So, if the test data is too different, the model might struggle with predictions Ibrahimi et al, (2017). adopted a more comprehensive approach, utilizing three distinct models for groundwater level prediction of the surface water table in the Saïss Plain (North ofMorocco).…”

mentioning

confidence: 99%

A Review of Geological and Climatic Variables in Groundwater Availability Prediction in Africa: Machine Learning Approaches

Touré,

Boateng,

Gidigasu

et al. 2024

Preprint

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Groundwater is crucial for Africa's potable water supply, agriculture, and economic development. However, the continent faces challenges with groundwater scarcity due to factors like population growth, climate change, and over exploitation. Over the past ten years, machine learning has been increasingly and successfully used in groundwater level prediction across the world. This review paper explores the application of machine learning techniques in predicting groundwater levels in Africa. The methodology involved downloading relevant papers, identifying and categorizing the machine learning algorithms employed, and quantifying their use. Geological and climatic variables were also identified, analyzed and categorized to measure their usage frequency. The different algorithms and input variables extracted from each paper are graphically represented in this document highlighting the most employed ones. The findings suggest that the available literature on this topic in Africa is limited compared to the rest of the world. Tree-based algorithms are commonly used in machine learning in Africa, and the most employed input variables are related to geomorphology and temperature. The study highlights the potential of machine learning in improving water resource management and decision-making in the region.

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Comparative Study of the Three Models (ANN-PMC), (DWT-ANN-PMC) and (MLR) for Prediction of the Groundwater Level of the Surface Water Table in the Saïss Plain (North of Morocco)

Cited by 5 publications

References 21 publications

Predicting Monthly Streamflow Using a Hybrid Wavelet Neural Network: Case Study of the Çoruh River Basin

Predicting Monthly Streamflow Using a Hybrid Wavelet Neural Network: Case Study of the Çoruh River Basin

Application of Several Artificial Intelligence Models for Forecasting Meteorological Drought Using the Standardized Precipitation Index in the Saïss Plain (Northern Morocco)

A Review of Geological and Climatic Variables in Groundwater Availability Prediction in Africa: Machine Learning Approaches

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