Sensitivity analysis of groundwater level in Jinci Spring Basin (China) based on artificial neural network modeling

Li, Xian; Shu, Longcang; Yin, Daqiang; Wen, Jiahong

doi:10.1007/s10040-012-0843-5

Cited by 29 publications

(12 citation statements)

References 23 publications

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“…Artificial neural networks have been applied also in scientific fields such as geology (Subbaiah, 2011;Xia-Ting, Young Jia, & Jian Guo, 1996;Zhu et al, 2013), hydrology (Adamowski & Chan, 2011;Ghose, Panda, & Swain, 2010;Kumar, Raghuwanshi, & Singh, 2010;Li, Shu, Liu, Yin, & Wen, 2012) or surface protection (Ambro zi c & Turk, 2003;Gruszczy nski, 2007;Jung, Cheon, & Choi, 2005;Kim, Lee, & Oh, 2008;Oh & Lee, 2011;Park, Choi, Jin Lee, & Lee, 2012;Pawlu s, 2007;Yang & Xia, 2013;Zhang, Liu, & Liu, 2011).…”

Section: Methodsmentioning

confidence: 97%

Suitability assessment of artificial neural network to approximate surface subsidence due to rock mass drainage

Hejmanowski

Witkowski

2015

Journal of Sustainable Mining

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Drainage of underground mineArtificial neural networks MLP a b s t r a c t Based on the previous studies conducted by the authors, a new approach was proposed, namely the tools of artificial intelligence. One of neural networks is a multilayer perceptron network (MLP), which has already found applications in many fields of science. Sequentially, a series of calculations was made for different MLP neural network configuration and the best of them was selected. Mean square error (MSE) and the correlation coefficient R were adopted as the selection criterion for the optimal network. The obtained results were characterized with a considerable dispersion. With an increase in the amount of hidden neurons, the MSE of the network increased while the correlation coefficient R decreased.Similar conclusions were drawn for the network with a small number of hidden neurons.The analysis allowed to select a network composed of 24 neurons as the best one for the issue under question. The obtained final answers of artificial neural network were presented in a histogram as differences between the calculated and expected value.

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Section: Methodsmentioning

confidence: 97%

Suitability assessment of artificial neural network to approximate surface subsidence due to rock mass drainage

Hejmanowski

Witkowski

2015

Journal of Sustainable Mining

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“…The approximate time response sequence was: (16) Through inverse accumulation restoration, i.e. Equation 13, we obtained the simulated Jinci Springs flow (Table 3).…”

Section: The Grey System Gm (1 N) Model With Time Lagmentioning

confidence: 99%

“…In order to study the relationship between spring flow and precipitation, Hao et al [15] proposed a correlation analysis method with time lag to distinguish the effects of precipitation variation and human factors on the drying up of Jinci Springs. Li et al [16] analyzed the sensitivity of groundwater level in Jinci Springs basin to anthropogenic activity, using an artificial neural network model. Their results showed that coal mining drainage was the most significant human factor, which had a great influence on the groundwater level in Jinci Springs basin.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Karst Spring Flow Cessation Using Grey System Model

Guo¹,

Yeh²,

Hao³

2019

Preprint

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Globally karst aquifers store large amount of precious water and create beautiful karst springs in many places. However, most of the karst springs flow declined, and some of the karst springs dried up with the effects of extensive groundwater development and climate variation. For example, Jinci Springs (China) is known for the beautiful landscape it created and large area of paddy fields it irrigated. Unfortunately, it dried up in May 1994. For better understanding of the hydrological processes of karst springs, this study introduced grey system models to quantify spring flow taking Jinci Springs as an example. Based on the characteristics of Jinci Springs flow, the spring flow was divided into two stages: the first stage (1954-1960), when the spring flow was affected only by climate variation; and the second stage (1961-1994), when the flow was impacted by both climate variation and anthropogenic activities. Results showed that the Jinci Springs flow had strong relations with precipitation occurring one year and three years earlier in the first stage. Subsequently, a grey system GM (1, 3) model with one-year and three-year lags was set up for the first stage. By using the GM (1, 3) model, we simulated the spring flow in the second stage under effects of climate variation only. Subtracting the observed spring flow from the simulated flow, we obtained the contribution of anthropogenic activities to Jinci Springs cessation. The contribution of anthropogenic activities and climate variation to Jinci Springs cessation was 1.46m3/s and 0.62m3/s, respectively. Finally, each human activity causing spring flow decline was estimated.

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“…Significantly, the most popular research topic of the BP neural network mainly involves sensitivity analysis, which aims to measure the effect of input parameters on output and acquire the most powerful input parameters [12,13]. However, there is hardly any research attempted to identify appropriate combination of input to acquire better output, especially for P2P lending in perspective of borrowers.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Borrowing Limit and Interest Rate in P2P System: From Borrowers’ Perspective

Tang

2018

Scientific Programming

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P2P (peer-to-peer) lending is an emerging online service that allows individuals to borrow money from unrelated person without the intervention of traditional financial intermediaries. In these platforms, borrowing limit and interest rate are two of the most notable elements for borrowers, which directly influence their borrowing benefits and costs, respectively. To that end, this paper introduces a BP neural network interval estimation (BPIE) algorithm to predict the borrowers' borrowing limit and interest rate based on their characteristics and simultaneously develops a new parameter optimization algorithm (GBPO) based on the genetic algorithm and our BP neural network predictive model to optimize them. Using real-world data from http://ppdai.com, the experimental results show that our proposed model achieves a good performance. is research provides a new perspective from borrowers in exploring the P2P lending. e case base and proposed knowledge are the two contributions for FinTech research.

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Sensitivity analysis of groundwater level in Jinci Spring Basin (China) based on artificial neural network modeling

Cited by 29 publications

References 23 publications

Suitability assessment of artificial neural network to approximate surface subsidence due to rock mass drainage

Suitability assessment of artificial neural network to approximate surface subsidence due to rock mass drainage

Investigation of Karst Spring Flow Cessation Using Grey System Model

Optimizing the Borrowing Limit and Interest Rate in P2P System: From Borrowers’ Perspective

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