Figure 3 - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Source publication
In recent decades, due to the population growth and low precipitation, the overexploitation of ground water resources has become an important issue. To ensure a sustainable scheme for these resources, understanding the behavior of the aquifers is a key step. This study takes a numerical modeling approach to investigate the behavior of an unconfined...
Contexts in source publication
Context 1
... using PF-MLPG, précised values of uncertain parameters were achieved. Figure 3 shows the flowchart of this work. Figure 3 shows the flowchart of the PF-MLPG model. ...
Similar publications
Biskra region currently shows signs of stress and a high risk of groundwater contamination by various chemicals and pesticides. For this purpose, a modified integrated susceptibility index (SI) is coupled with remote sensing (RS) and WetSpass model to assess the sensitivity of the groundwater and the risk of pollution in the most exploited aquifer...
Citations
... Precipitation and evaporation are achieved by Birjand precipitation stations. Inflow and outflow groundwater fronts are derived from Mohtashami et al. (2022) and their values assigned to dynamic model according to the latest report of SKHRW (2021a, 2021b) and Mohtashami et al. (2019a, b). Information of 190 extraction wells is derived from Mohtashami et al. (2017a, b). ...
Due to the growth of population and industrial advancements in Iran, especially Birjand, the use of groundwater makes the aquifer’s balance becomes negative. This negatively affects both quantity and quality conditions of groundwater. To prevent this, suitable water management is necessary for Birjand aquifer. In this study, to overcome negative groundwater balance, the concept of “adjustment factors” is presented. These factors are applied to all types of consumption and make the groundwater balance to be positive. For this aim, a dynamic model of groundwater resources in Birjand aquifer is created in Vensim software. This model helps to determine the groundwater balance. Then, with using dynamic model and particle filter approach in MATLAB software, minimum adjustment factors are achieved. All data and information of Birjand aquifer between 2004 and 2021 are entered into Vensim model; then, under three scenarios, the groundwater balance is computed for the next 5 years (2022–2026). Three scenarios are normal, dry, and wet conditions. In the next step, with the help of particle filter, the minimum adjustment factors for two types of consumptions including agricultural and industrial are computed. The results show that the adjustment factors for all consumptions in dry conditions are much higher than others. For instance, in 2026, the adjustment factor for agriculture in dry conditions is 0.081 while in normal and wet conditions is 0.75 and 0.031, respectively. Also, the findings indicate that applying these adjustment factors to groundwater model has successful results and make the groundwater balance to be positive.
... This is done to improve the initial conditions and values of parameters for a new implementation or modify the model outputs. Data assimilation is a process by which the real measured and observed data are entered to the cycles of the prediction stage (Mohtashami et al. 2022). They are combined with the model output results to yield an optimal estimate of the system's governed conditions. ...
As an arid country, Oman's falaj flowrate directly affects the environmental, agricultural, and ecological characteristics of its regions. For this reason, it is vitally important for water decision managers, farmers, and governments to have an accurate prediction of falaj’s flowrate. The present research aims to develop a new method for flowrate of falaj modeling named particle filter-Bayesian network (PF-BN) based on data assimilation method and Bayesian Network. A falaj in Birkat Al-Mouz, Nizwa, Oman, named Al-Khatmain is selected as a case study. This falaj, which is listed as a UNESCO heritage, plays an important role in agriculture (mainly palms, bananas and mangoes). A weekly flow dataset was collected by the authors from 2021 to 2022. Results of PF-BN are compared with the conventional BN model. The models are all written in MATLAB. As a result, four input patterns were introduced to the models. In these patterns, the flow rate of previous weeks or months is taken into account. A total of four patterns including: M1, M2, M3 and M4, are considered. According to results, data assimilation leads to more accurate responses, as three error criteria are calculated. There are three criteria here: RMSE, NRMSE, and NSE. In terms of efficiency indices, M4(PF-BN) is 0.87, 0.011, 0.93 and M4(BN) is 2.4, 0.0315, 0.89. According to this error values M4(PF-BN) performance is satisfactory.
... W i is the weight value of i-th node, ; is the shape function, Dt is the time step (1 month), V is the domain (aquifer). K, U and F are the stiffness, unknown and force body matrix, respectively (Mohtashami et al. 2022b). ...
Qanat is an ancient water harvesting technology which is used for irrigation, agriculture and drinking in many countries of the world. A key element of preserving this heritage is calculating their protection area. In this study, the main aim is depiction of the hydraulic harim of qanat with the usage of the meshless numerical method. Afterward, the qanat package has been added to this model to delineate the hydraulic harim of the qanat. Two qanat systems are shown here: one in a standard aquifer and the other in a real test case, namely Haji-Abad qanat, located in the Birjand unconfined aquifer. In the real test case, the hydraulic harim of Haji-Abad qanat with 10 collection shafts is depicted in 3 time sections as well. The time sections are 3 years after extraction (2014), 6 years (2017) and 11 years (2022). The findings stated that time makes the harim area larger. In addition, the hydraulic harim is extended to areas with a higher hydraulic conductivity coefficient. Ultimately, we have delineated the hydraulic harim for 2040 and unfortunately, it is influenced by buildings. Therefore, it is necessary to find the hydraulic harim of qanats in order to protect this traditional heritage.
HIGHLIGHT
This method is applicable for all qanats with different conditions.;
... MLPG uses two functions in its main body code, weight and shape function. They are determined by W and ∅ in the following equations (Mohtashami et al. 2022b). ...
In this research, a simulation–optimization (S/O) model is used in order to estimate aquifer parameters on two aquifers. In this model, meshless local Petrov–Galerkin (MLPG) is used for simulation purpose and modified teaching–learning-based optimization (MTLBO) algorithm is engaged as optimization model. Linking these two powerful models generates a S/O model named MLPG-MTLBO. The proposed model is applied on two aquifers: a standard and a real field aquifer. In standard aquifer, parameters are only transmissivity coefficients in x and y direction for three zones. The acquired results by MLPG-MTLBO are really close to true values. This fact presents the power of MLPG-MTLBO inverse model. Therefore, it is applied on field aquifer. Unconfined aquifer of Birjand recognized as real case study. Parameters which are needed to be estimated are specific yields and hydraulic conductivity coefficients. These parameters are computed by MLPG-MTLBO and entered to the groundwater flow model. The achieved groundwater table compared with observation data and RMSE is calculated. RMSE value is 0.356 m; however, this error criterion for MLPG and FDM is 0.757 m and 1.197 m, respectively. This means that estimation is precise and makes the RMSE to reduce from 0.757 to 0.356 m, and also, MLPG-MTLBO is an accurate model for this aim.
... Sequential Monte-Carlo methods-alternatively termed particle filters-have been used in some geoscience applications (Leisenring and Moradkhani, 2011;Field, 2018;Mohtashami et al., 2022). However, they are computationally too expensive to be of any practical use in high-dimensional geoscience applications. ...
... Sequential Monte-Carlo methods-alternatively termed particle filters-have been used in some geoscience applications (Leisenring and Moradkhani, 2011;Field, 2018;Mohtashami et al., 2022). However, they are computationally too expensive to be of any practical use in high-dimensional geoscience applications. ...
... Sequential Monte-Carlo methods-alternatively termed particle filters-have been used in some geoscience applications (Leisenring and Moradkhani, 2011;Field, 2018;Mohtashami et al., 2022). However, they are computationally too expensive to be of any practical use in high-dimensional geoscience applications. ...
Forests provide important ecosystem services, several of them related to water. The interconnections between rainfall interception and the water cycle vary among the forest types. Tropical forests are one of the most relevant biodiversity shelters, with several endemic plant and animal species. These forests provide key ecohydrological services by directly acting on biogeochemical cycles and water supply. Understanding these interconnections is crucial for better planning forest conservation and reforestation initiatives, especially under climate change conditions. Therefore, a set of techniques and devices is required to monitor rainfall-forest interface processes in tropical forests as they are very complex ecosystems. In this chapter, we intend to assess rainfall-forest interface processes as well as some of the methods that have been used for quantifying rainfall interception in forests, contributing to the ecohydrological studies.
The study presents the development of the Meshless Local Petrov–Galerkin (MLPG) method to model and predict contaminant transport in porous media which is known as an important issue to prevent the pollution propagation in groundwater. The research focuses on the Rafsanjan Plain in southeastern Iran as a real case study, where no studies of meshless methods have been done. Computational models were implemented using MATLAB, integrating Radial Basis Functions (RBF) as the interpolation technique. The process commenced by establishing a groundwater flow model, where hydraulic head served as the foundational parameter for determining seepage velocity under unsteady-state conditions. Subsequently, the contamination transport was simulated within the aquifer for a period of six months. Comparative analysis between observed empirical data and the modeled values in both flow dynamics and contaminant transport revealed a significant and reasonable alignment. Analysis of the parameters showed that the value of the shape parameter (\({\alpha }_{{\text{c}}})\) can have the greatest impact on achieving a more accurate value. In this research, with \({\alpha }_{{\text{c}}}\)=6 in the contamination transport model, the most reliable outputs were obtained compared to the observed values. Furthermore, the assessment of the coefficient of determination (R2 = 0.97) was conducted to evaluate the acceptability of the calibrated parameters. The high R2 value suggests a robust correlation between the observed and simulated data, indicating that the model's parameters are within an acceptable range. This finding underscores the accuracy and reliability of the developed MLPG method for analyzing contaminant transport in unconfined aquifers, particularly in the context of the Rafsanjan Plain.
One of the applications that machine learning can offer to the world of Engineering and Fluid Mechanics in particular is the calibration of models making it possible to approximate the representation of a particular phenomenon. Indeed, the computational cost generated by some fluid mechanics models pushes scientists to use other models close to the original models but less computationally intensive in order to facilitate their handling. Among the different approaches used: machine learning coupled with some optimization methods and algorithms in order to reduce the computation cost induced. This paper proposes a new framework called OPTI-ENS: a new flexible, optimized and improved method, to calibrate a physical model, called the wake oscillator (WO), which simulates the vibratory behaviors of overhead line conductors. An approximation of a heavy and complex model called the strip theory (ST) model. OPTI-ENS is composed of an ensemble machine learning algorithm (ENS) and an optimization algorithm of the WO model so that the WO model can generate the adequate training data as input to the ENS model. ENS model will therefore take as input the data from the WO model and output the data from the ST model. As a benchmark, a series of Machine learning models have been implemented and tested. The OPTI-ENS algorithm was retained with a best Coefficient of determination (R2 Score) of almost 0.7 and a Root mean square error (RMSE) of 7.57e-09. In addition, this model is approximately 170 times faster (in terms of calculation time) than an ENS model without optimization of the generation of training data by the WO model. This type of approach therefore makes it possible to calibrate the WO model so that simulations of the behavior of overhead line conductors are carried out only with the WO model.
