Reliability evaluation of groundwater quality index using data-driven models

Groundwater is an irreplaceable resource for irrigation and drinking in the North China Plain, and the quality of groundwater is of great importance to human health and social development. In this study, using the information from 59 groups of groundwater samples, groundwater quality conditions for irrigation and drinking purposes in an agricultural region of the North China Plain were analyzed. The groundwater belongs to a Quaternary loose rock pore water aquifer. The depths of shallow groundwater wells are 20–150 m below the surface, while the depths of deep groundwater wells are 150–650 m. The sodium adsorption ratio (SAR), sodium percentage (%Na), residual sodium carbonate (RSC), magnesium hazard (MH), permotic index (PI) and electrical conductivity (EC) were selected as indexes to evaluate the shallow groundwater suitability for irrigation. What’s more, the deep groundwater suitability for drinking was assessed and the human health risk of excessive chemicals in groundwater was studied. Results revealed that SAR, Na% and RSC indexes indicated the applicability of shallow groundwater for agricultural irrigation in the study area. We found 57.1% of the shallow groundwater samples were located in high salinity with a low sodium hazard zone. The concentrations of fluorine (F−) in 79.0% of the deep groundwater samples and iodine (I−) in 21.1% of the deep groundwater samples exceeded the permissible limits, respectively. The total hazard quotient (HQ) values of fluorine in over half of the deep groundwater samples exceeded the safety limits, and the health risk degree was ranked from high to low as children, adult females and adult males. In addition to natural factors, the soil layer compression caused by groundwater over-exploitation increased the fluorine concentration in groundwater. Effective measures are needed to reduce the fluorine content of the groundwater of the study area.

Section: Methods Of Suitability Evaluation For Irrigation Purposementioning

confidence: 99%

Section: Methods Of Suitability Evaluation For Drinkingmentioning

confidence: 99%

Evaluation of Groundwater Suitability for Irrigation and Drinking Purposes in an Agricultural Region of the North China Plain

Guo

Wang

et al. 2021

“…Several studies also focus on the quality of groundwater and of water bodies such as rivers, watersheds, and coastal environments. This plays an important role in determining its impact on public health and the environment [7,8]. Many countries in Europe have started developing river monitoring systems in their national monitoring programs based on macrophytes [9,10], According to the Water Framework Directive (WFD) [11], assessment of freshwater is based on ecological status consisting of biological indicators (fish, macroinvertebrates, phytoplankton, phytobenthos, and macrophytes), supported by water quality and physical conditions of ecosystems.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Gebler et al [18] and Krtolica et al [19] showed the relationship between macrophyte indices, biological diversity indices, and water quality parameters, hydromorphological indices as explanatory variables using artificial neural networks [20]. Further, Najafzadeh et al [7] evaluated groundwater quality at the Rafsanjan basin, Iran, using artificial intelligence models such as M5 Model Tree (MT), Evolutionary Polynomial Regression (EPR), Gene-Expression Programming (GEP), and Multivariate Adaptive Regression Spline (MARS). These data-driven techniques have been widely used to study groundwater resources, artificial aquifer recharge, and effects of global warming on groundwater quality.…”

Section: Introductionmentioning

confidence: 99%

Non-Linear Visualization and Importance Ratio Analysis of Multivariate Polynomial Regression Ecological Models Based on River Hydromorphology and Water Quality

Shah

Jagupilla

Vaccari

et al. 2021

Multivariate polynomial regression (MPR) models were developed for five macrophyte indices. MPR models are able to capture complex interactions in the data while being tractable and transparent for further analysis. The performance of the MPR modeling approach was compared to previous work using artificial neural networks. The data were obtained from hydromorphologically modified Polish rivers with a widely varying water quality. The modeled indices were the Macrophyte Index for Rivers (MIR), the Macrophyte Biological Index for Rivers (IBMR), and the River Macrophyte Nutrient Index (RMNI). These indices measure the trophic and ecological status of the rivers. Additionally, two biological diversity indices, species richness (N) and the Simpson index (D), were modeled. The explanatory variables were physico-chemical properties depicting water quality and river hydromorphological status indices. In comparison to artificial neural networks, the MPR models performed similarly in terms of goodness of fit. However, the MPR models had advantages such as model simplicity and ability to be subject to effective visualization of complex nonlinear input–output relationships, as well as facilitating sensitivity analysis using importance ratios to identify effects of individual input variables.

“…Currently, machine learning is often used as a data-driven model in hydrology. For instance, data-driven models have been used to assess groundwater quality [5][6][7] and to predict drought [8][9][10][11] and evapotranspiration [12][13][14]. Hydrological models using artificial neural networks have seen vigorous increases in machine learning [6,[9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Development and Assessment of Water-Level Prediction Models for Small Reservoirs Using a Deep Learning Algorithm

Kusudo¹,

Yamamoto²,

Kimura³

et al. 2021

In this study, we aimed to develop and assess a hydrological model using a deep learning algorithm for improved water management. Single-output long short-term memory (LSTM SO) and encoder-decoder long short-term memory (LSTM ED) models were developed, and their performances were compared using different input variables. We used water-level and rainfall data from 2018 to 2020 in the Takayama Reservoir (Nara Prefecture, Japan) to train, test, and assess both models. The root-mean-squared error and Nash–Sutcliffe efficiency were estimated to compare the model performances. The results showed that the LSTM ED model had better accuracy. Analysis of water levels and water-level changes presented better results than the analysis of water levels. However, the accuracy of the model was significantly lower when predicting water levels outside the range of the training datasets. Within this range, the developed model could be used for water management to reduce the risk of downstream flooding, while ensuring sufficient water storage for irrigation, because of its ability to determine an appropriate amount of water for release from the reservoir before rainfall events.