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Evaluation of shallow groundwater quality under impact zones of livestock and poultry farms using self-organizing maps

100%

Chen J. , Gui H.

2023

tom Vol. 71, no. 5

2465--2479

The shifting of livestock and poultry production systems from traditional small householder farms to semi-intensive and intensive farms has led to a gradual deterioration in the quality of shallow groundwater, which has attracted considerable attention from researchers. In this study, a combination of self-organizing map technology was used to identify the effects of livestock and poultry farms on shallow groundwater hydrochemistry. NO3–N content in the livestock and poultry farm water samples in summer and winter, as well as the NH4–N and NO2–N content in the water samples of livestock and poultry farm, respectively, in winter, were more vulnerable to external influences. Agricultural and industrial activities were important sources of Cl- and SO42- leaching in shallow groundwater in the study area. Silicate weathering is an important source of conventional ions in the shallow groundwater at these two sites. The water quality at livestock farms was mainly affected by farm activities and agricultural pollution in summer and winter, whereas that at poultry farms was mainly affected by industrial sources and natural sources.

Recognition of mine water-inrush sources using artificial neural network

88%

Qinglong Z. , Herrera J. , Hidalgo A.

2023

tom Vol. 30

147--156

In the process of excavating and mining, water-inrush episodes induced by a number of geological or human factors is a complex geological hazard and often lead to disastrous consequences, making an accurate prediction before an inrush accident is difficult because there are so many factors and interactions between factors are related in such hazard, No matter how accurate a risk assessment approach is, it can not 100% guarantee that every water inrush accident can be accurately predicted. Until so far, inrush accidents are still occurring every year all over the world, especially in developing countries. For inrush accidents in underground mining, the first and also the critical step of controlling the accident is to find out the related inrush sources, accurately identifying which aquifer or which water body is directly related to the inrush accident is the critical step of controlling water volume and reducing casualties and economic losses. In this study, method of using artificial neural network (ANN) to identify the water-inrush sources is proposed, by establishing a back propagation neural network (BPNN) to train, test and predict the sample data selected from Jiaozuo mine area, results show that ANN is an effective approach to identify water sources.

Hydrochemical characteristics and water quality assessment of Lake Dayet Erroumi - Khemisset, Morocco

75%

El Qryefy M. , Ouardi J. , Najy M. , Belghyti D. , El Kharrim K. , El Qryefy M. , Ouardi J. , Najy M. , Belghyti D. , El Kharrim K.

Hydro-chemical characteristic of the Czarniawka River

63%

Nocon W. , Kostecki M.

nr 2