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首页> 外文期刊>Current Science: A Fortnightly Journal of Research >A hybrid-wavelet artificial neural network model for monthly water table depth prediction
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A hybrid-wavelet artificial neural network model for monthly water table depth prediction

机译：用于月度水位深度预测的混合小波人工神经网络模型

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摘要

Groundwater is an essential natural resource in the country to fulfil the irrigation, domestic, industrial and other needs. In order to ensure sustainable use of groundwater resources, the groundwater level is used as an important indicator for balancing the groundwater withdrawal rate and replenishment rate through the recharge. Quantitatively, the recharge rate is governed by various complex large-scale hydrological processes and hence achievement of sustainability of groundwater supplies, through sustainable withdrawal rate is a complicated issue. In the present study, a data-driven prediction model by combining discrete wavelet transform (DWT) with artificial neural network (ANN) called as wavelet artificial neural network (WANN) is proposed for the groundwater table prediction. The simulation results obtained by regular ANN model were compared with those obtained by WANN model to prove the superiority of the latter model over the former. WANN model was developed using decomposed signals of rainfall, evapotranspiration and water table depth time series as inputs in the ANN model to arrive at a prediction of monthly fluctuation of the groundwater table Rainfall time series was decomposed using Haar wavelet at third decomposition level and evapotranspiration and water table depth time series was decomposed using Daubechies wavelet at second decomposition level. The RMSE value of ANN and WANN model during validation were found to be 0.3648 m and 0.1695 m respectively, which showed decrease in RMSE value by 0.195 m when WANN was applied. Model efficiencies of ANN and WANN model during validation were 84.65% and 95.68%, indicating excellent improvement of model accuracy after applying WANN. Hence, the proposed WANN model seems to be a promising tool to predict the monthly water table fluctuation.
机译：地下水是该国一个基本的自然资源，以满足灌溉，国内，工业和其他需求。为了确保可持续利用地下水资源，地下水位被用作通过充电平衡地下水退出率和补货率的重要指标。定量方面，通过可持续的戒断率是一种复杂的问题，通过各种复杂的大规模水文过程来控制各种复杂的大规模水文过程，从而实现地下水供应的可持续性。在本研究中，提出了通过将具有作为小波人工神经网络（Wann）组合的离散小波变换（DWT）与被称为小波人工神经网络（WANN）的分立小波变换（DWT）进行数据驱动的预测模型。将常规ANN模型获得的模拟结果与WANN模型获得的仿真结果进行了比较，以证明前者模型的优越性。 Wann模型是使用降雨的分解信号开发的，作为ANN模型中的输入，以预测地下水表的每月波动预测，在第三分解水平和蒸发血管蒸腾和蒸发的哈尔小波中分解。水台深度时间序列在第二分解水平下使用Daubechies小波分解。验证期间ANN和WANN模型的RMSE值分别为0.3648 m和0.1695米，当申请WANN时，RMSE值下降0.195米。验证期间ANN和WANN模型的模型效率为84.65％和95.68％，表明在申请WANN后，模型精度的良好提高。因此，拟议的WANN模型似乎是预测月度水位波动的有希望的工具。

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