Department of Civil Engineering, University of Sistan and Baluchestan, Zahedan, Iran.
Department of Civil Engineering, University of Sistan and Baluchestan, Zahedan, Iran.
J Contam Hydrol. 2019 Jan;220:6-17. doi: 10.1016/j.jconhyd.2018.10.010. Epub 2018 Nov 14.
Drought is one of the most significant natural phenomena affecting different aspects of human life and the environment. Due to water scarcity, prediction of water quality reduction is very crucial for urban and rural communities. This study contributes by applying artificial neural network and modified fuzzy clustering techniques to estimate the drops in potential drinking water quality in the GIS environment. In this research, the probability of occurrence of adverse annual changes in the water quality of drinking water is estimated. The model was tested using real instances of the southeast aquifers, the regions of the central parts of the IRAN and especially the significant portions of the aquifers of the east area. To validate the model, the data adequacy test and the standardization of the drought index are used. The results of the lowest available water quality and the highest drought using ANNs show that the qualitative stress conditions in large part of the country's aquifers are in unfavorable conditions. Evidence from this research shows that the aquifers in these areas are expected to have severe drought stress and poor quality class status. Also, the computational results indicate that the modified clustering method increases the efficiency of the prediction model as against the previous research. The outcomes do not show a relatively favorable state of drinking water quality for some aquifers in the country. However, the conditions for quantitative changes in the depth of water, based on the predicted results of ANN, are considered critical. The generated maps demonstrate that about 64% of the study area is subjected to a severe deterioration in the quality of drinking water if the current trend continues in the exploitation of aquifers. As a result, the main finding the present study is that the probability of groundwater quality decline is significant in many aquifers in the country.
干旱是影响人类生活和环境各个方面的最重要的自然现象之一。由于水资源短缺，预测水质下降对于城乡社区非常重要。本研究通过应用人工神经网络和改进的模糊聚类技术，在 GIS 环境中估计潜在饮用水水质下降，做出了重要贡献。在这项研究中，估计了饮用水水质不利年变化发生的概率。该模型使用伊朗中部地区东南部含水层的实际实例进行了测试，特别是该地区东部含水层的重要部分。为了验证模型，使用了数据充足性测试和干旱指数的标准化。使用 ANNs 对最低可得水质和最高干旱的结果表明，该国大部分含水层的定性压力条件处于不利条件。该研究的证据表明，这些地区的含水层预计将面临严重的干旱压力和较差的水质状况。此外，计算结果表明，改进的聚类方法提高了预测模型的效率，优于以前的研究。研究结果表明，该国一些含水层的饮用水水质状况并不理想。然而，根据 ANN 的预测结果，水深度的定量变化条件被认为是关键的。生成的地图表明，如果继续按照目前的趋势开采含水层，研究区约有 64%的地区将面临饮用水水质严重恶化的问题。因此，本研究的主要发现是，该国许多含水层的地下水质下降的可能性很大。
