Groundwater salinity prediction using adaptive neuro-fuzzy inference system methods: a case study in Azarshahr, Ajabshir and Maragheh plains, Iran

Nazari, Hosnie; Taghavi, Behnam; Hajizadeh, Farnusch

doi:10.1007/s12665-021-09455-3

Cited by 12 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This heterogeneity is reflected in the wide range of groundwater EC values shown in Table 1. Our results agree with those reported by other authors who did not consider temperature as an influential parameter in the development of their groundwater EC prediction models [13,27].…”

Section: Variables Selection For Development Of Theoretical Modelssupporting

confidence: 93%

Artificial Neural Networks to Predict Electrical Conductivity of Groundwater for Irrigation Management: Case of Campo de Cartagena (Murcia, Spain)

Mateo,

Más-López,

García-del-Toro

et al. 2024

Agronomy

View full text Add to dashboard Cite

Groundwater is a crucial water resource, particularly in regions with intensive agriculture and a semi-arid climate, such as Campo de Cartagena (Murcia, Spain). Groundwater salinity in the area can be attributed to hydrogeological characteristics, irrigation return water, or even marine intrusion and communication between aquifers. The management of these waters is essential to maintain sustainable agriculture in the area. Therefore, two groundwater salinity prediction models were developed, a backpropagation artificial neural network (ANN) model and a multiple linear regression (MLR) model, based on EC (electrical conductivity) data obtained from official information sources. The data used were the bicarbonate, calcium, chloride, magnesium, nitrate, potassium, sodium, and sulphate concentrations, as well as EC, pH, and temperature, of 495 water samples from 38 sampling stations between 2000 and 2023. Variables with the least influence on the model were discarded in a previous statistical analysis. Based on seven evaluation metrics (RMSE, MAE, R2, MPE, MBE, SSE, and AARD), the ANN model showed a sligntly better accuracy in predicting EC compared to the MLR model. As a result, the ANN model, together with crop tolerance to EC, may be an effective tool for groundwater irrigation management in these areas.

show abstract

Section: Variables Selection For Development Of Theoretical Modelssupporting

confidence: 93%

Artificial Neural Networks to Predict Electrical Conductivity of Groundwater for Irrigation Management: Case of Campo de Cartagena (Murcia, Spain)

Mateo,

Más-López,

García-del-Toro

et al. 2024

Agronomy

View full text Add to dashboard Cite

show abstract

“…Many scholars have stated their tendency regarding the use of hybrid methods for similar issues (e.g., sediment concentration [85] and salinity [86] predictions). The reason for the development of such models can be the use of an optimization technique in the position of a trainer algorithm.…”

Section: Further Discussionmentioning

confidence: 99%

Predicting the Degree of Dissolved Oxygen Using Three Types of Multi-Layer Perceptron-Based Artificial Neural Networks

2021

View full text Add to dashboard Cite

Predicting the level of dissolved oxygen (DO) is an important issue ensuring the sustainability of the inhabitants of a river. A prediction model can predict the DO level using a historical dataset with regard to water temperature, pH, and specific conductance for a given river. The model can be built using sophisticated computational procedures such as multi-layer perceptron-based artificial neural networks. Different types of networks can be constructed for this purpose. In this study, the authors constructed three networks, namely, multi-verse optimizer (MVO), black hole algorithm (BHA), and shuffled complex evolution (SCE). The networks were trained using the datasets collected from the Klamath River Station, Oregon, USA, for the period 2015–2018. We found that the trained networks could predict the DO level of 2019. We also found that both BHA- and SCE-based networks could predict the level of DO using a relatively simple configuration compared to that of MVO. From the viewpoints of absolute errors and Pearson’s correlation coefficient, MVO- and SCE-based networks performed better than BHA-based networks. In synopsis, the authors recommend MVO- and MLP-based artificial neural networks for predicting the DO level of a river.

show abstract

“…The ability of ML models to model groundwater salinity has been demonstrated via the establishment of a linear or non-linear relationship between water salinity and its control parameters (such as water table, evaporation, and distance to saltwater bodies) and using those relationships for the prediction of water salinity for regions with unavailable data points 39 , 40 . Various versions of ML models have been reported in the literature, such as artificial neural network (ANN) 41 – 45 , support vector machine (SVM) 46 – 48 , adaptive neuro-fuzzy inference system (ANFIS) 49 , 50 , ensemble ML models 38 , 51 , 52 , group method of data handling (GMDH) 53 , and Gaussian process scheme 54 . The significant limitations associated with predictive ML models (1) the need for adequate input variables to explain the target data that may not be available everywhere 55 , 56 , (2) the influence of well excessive pumping 57 , 58 , (3) the reliability of the learning process of the predictive model where essential hyperparameters are optimized 59 , 60 , (4) coupled ML models where a pre-processing technique was integrated for data time series decomposition 61 , 62 .…”

Section: Introductionmentioning

confidence: 99%

Computational assessment of groundwater salinity distribution within coastal multi-aquifers of Bangladesh

Jamei

Karbasi

Malik

et al. 2022

Sci Rep

View full text Add to dashboard Cite

The rising salinity trend in the country’s coastal groundwater has reached an alarming rate due to unplanned use of groundwater in agriculture and seawater seeping into the underground due to sea-level rise caused by global warming. Therefore, assessing salinity is crucial for the status of safe groundwater in coastal aquifers. In this research, a rigorous hybrid neurocomputing approach comprised of an Adaptive Neuro-Fuzzy Inference System (ANFIS) hybridized with a new meta-heuristic optimization algorithm, namely Aquila optimization (AO) and the Boruta-Random forest feature selection (FS) was developed for estimating the salinity of multi-aquifers in coastal regions of Bangladesh. In this regard, 539 data samples, including ten water quality indices, were collected to provide the predictive model. Moreover, the individual ANFIS, Slime Mould Algorithm (SMA), and Ant Colony Optimization for Continuous Domains (ACOR) coupled with ANFIS (i.e., ANFIS-SMA and ANFIS-ACOR) and LASSO regression (Lasso-Reg) schemes were examined to compare with the primary model. Several goodness-of-fit indices, such as correlation coefficient (R), the root mean squared error (RMSE), and Kling-Gupta efficiency (KGE) were used to validate the robustness of the predictive models. Here, the Boruta-Random Forest (B-RF), as a new robust tree-based FS, was adopted to identify the most significant candidate inputs and effective input combinations to reduce the computational cost and time of the modeling. The outcomes of four selected input combinations ascertained that the ANFIS-OA regarding the best accuracy in terms of (R = 0.9450, RMSE = 1.1253 ppm, and KGE = 0.9146) outperformed the ANFIS-SMA (R = 0.9406, RMSE = 1.1534 ppm, and KGE = 0.8793), ANFIS-ACOR (R = 0.9402, RMSE = 1.1388 ppm, and KGE = 0.8653), Lasso-Reg (R = 0.9358), and ANFIS (R = 0.9306) models. Besides, the first candidate input combination (C1) by three inputs, including Cl− (mg/l), Mg2+ (mg/l), Na+ (mg/l), yielded the best accuracy among all alternatives, implying the role importance of (B-RF) feature selection. Finally, the spatial salinity distribution assessment in the study area ascertained the high predictability potential of the ANFIS-OA hybrid with B-RF feature selection compared to other paradigms. The most important novelty of this research is using a robust framework comprised of the non-linear data filtering technique and a new hybrid neuro-computing approach, which can be considered as a reliable tool to assess water salinity in coastal aquifers.

show abstract

Groundwater salinity prediction using adaptive neuro-fuzzy inference system methods: a case study in Azarshahr, Ajabshir and Maragheh plains, Iran

Cited by 12 publications

References 14 publications

Artificial Neural Networks to Predict Electrical Conductivity of Groundwater for Irrigation Management: Case of Campo de Cartagena (Murcia, Spain)

Artificial Neural Networks to Predict Electrical Conductivity of Groundwater for Irrigation Management: Case of Campo de Cartagena (Murcia, Spain)

Predicting the Degree of Dissolved Oxygen Using Three Types of Multi-Layer Perceptron-Based Artificial Neural Networks

Computational assessment of groundwater salinity distribution within coastal multi-aquifers of Bangladesh

Contact Info

Product

Resources

About