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Characteristic of regional groundwater flow system: case modeling study in Longzici karst spring area, North China

Qiao Xiaojuan, Cheng Yu, Wang Dawei

Acta Geophysica

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2024

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Vol. 72, no. 1

323--339

EN

Karst spring water dynamic characteristics and its response to atmospheric precipitation are of great significance for water resources utilization under the background of climate change. This paper selects Longzici spring area, North China, as the study area. Based on a long series of spring water flow and precipitation data, the dynamic characteristics of spring flow were analyzed and the numerical simulation of the groundwater flow model was established. The results show that the groundwater kept the sustained decline over the past decades which is in a negative equilibrium state, with a storage variable of - 2.26 million m3/year. The sensitivity of spring flow to precipitation under different precipitation scenarios shows that the water level changes in the recharge and drainage areas are similar about (3-5 cm) and slightly larger than that in the runoff area(1.5 cm) when minimum rainfall (287.24 mm) happens. When the precipitation is at its maximum (867.66 mm), the water level change in the runoff area can reach 95 cm which is much larger than those in the recharge and discharge areas. The results indicate that Longzici karst spring has a relatively good regulation water resource capacity and the runoff area is more sensitive which plays an important role in response to climate change.

2

Water Source Analysis for Birra Peja: Focus on Physicochemical Attributes

Elshani Arsim, Loshi Indrit, Pehlivani Kastriot, Zhara Afrore

Ecological Engineering & Environmental Technology

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2023

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Vol. 24, iss. 8

194--201

EN

The study aims to determine whether the physicochemical attributes of different water sources, including the karstic spring of Radavc and three boreholes, meet the established brewing water quality standards and guidelines. This investigation also seeks to understand how the mineral composition of the water may impact the flavor profile and brewing efficiency of the beer produced by the brewery. In essence, the problem revolves around ensuring the availability of high-quality water for beer production and optimizing brewing processes based on water characteristics. These results indicate that the water from the Drini Bardhë source exhibits superior quality compared to the well water. Specifically, the Drini Bardhë water displays favourable pH levels and mineral content suitable for drinking water. However, the well water samples exhibit higher iron concentrations, potentially impacting the taste of the final products. Despite this, all samples show low levels of total coliforms, meeting the World Health Organization’s safety standards for consumption and production processes. Overall, this study emphasizes the significance of understanding the physicochemical attributes of water sources for breweries like Birra Peja. By tailoring water treatment and modification approaches based on these attributes, breweries can enhance brewing efficiency, consistency, and the final product’s sensory characteristics. This research contributes to the broader knowledge of water quality’s role in the brewing industry and provides valuable insights for optimizing beer production processes.

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Machine learning for predicting discharge fuctuation of a karst spring in North China

Cheng Shu, Qiao Xiaojuan, Shi Yaolin, Wang Dawei

Acta Geophysica

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2021

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Vol. 69, no. 1

257--270

EN

The quantitative analyses of karst spring discharge typically rely on physical-based models, which are inherently uncertain. To improve the understanding of the mechanism of spring discharge fuctuation and the relationship between precipitation and spring discharge, three machine learning methods were developed to reduce the predictive errors of physical-based groundwater models, simulate the discharge of Longzici spring’s karst area, and predict changes in the spring on the basis of long time series precipitation monitoring and spring water fow data from 1987 to 2018. The three machine learning methods included two artifcial neural networks (ANNs), namely multilayer perceptron (MLP) and long short-term memory–recurrent neural network (LSTM–RNN), and support vector regression (SVR). A normalization method was introduced for data preprocessing to make the three methods robust and computationally efcient. To compare and evaluate the capability of the three machine learning methods, the mean squared error (MSE), mean absolute error (MAE), and root-mean-square error (RMSE) were selected as the performance metrics for these methods. Simulations showed that MLP reduced MSE, MAE, and RMSE to 0.0010, 0.0254, and 0.0318, respectively. Meanwhile, LSTM–RNN reduced MSE to 0.0010, MAE to 0.0272, and RMSE to 0.0329. Moreover, the decrease in MSE, MAE, and RMSE was 0.0397, 0.1694, and 0.1991, respectively, for SVR. Results indicated that MLP performed slightly better than LSTM–RNN, and MLP and LSTM–RNN performed considerably better than SVR. Furthermore, ANNs were demonstrated to be prior machine learning methods for simulating and predicting karst spring discharge.

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Surface Karst Landforms of the Notranjska region (south-western Slovenia)

Gawor Ł., Jonczy I.

Geotourism / Geoturystyka

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2014

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nr 2

55--60

EN

The distribution and characteristics of surface karst landforms in the Notranjska region, exemplified in the Cerknisica and Rak river catchment, is presented. The geomorphology of the examined area, with respect to on the micro-, meso- and macro-forms division, is described. The course and dynamics of morphogenetic and geological processes are analyzed. A geotourist route linking the described landforms of surface karst is proposed.