小波-神经网络混合模型预测地下水水位

由于过量开采地下水,华北平原的许多城市出现地下水水位持续下降趋势,由此导致了许多严重的环境问题,如地下水枯竭、地面沉降和海水入侵等。为了准确预测城市地下水水位变化,利用小波变换的多尺度分析特征,建立了小波-神经网络混合模型(以下简称"混合模型"),并研究了其在地下水水位预测中的精度。利用北京市平谷区地下水水位观测资料,分别用BP网络和混合模型对该区地下水水位进行了预测。采用均方根误差(RMSE)、平均绝对误差(MAE)和线性相关系数(R)对模型预测的精度进行度量。预测结果表明:混合模型第1至第3个月的地下水水位平均绝对误差分别是0.535,0.598和0.634 m;而BP模型的平均绝对误差分别为0.566,0.824和0.940 m。混合模型的预测误差分别为BP模型的95%,73%和67%。使用混合模型能明显提高预测的精度,显著增加有效预测时段长度。     

A Comparative Study of Data-driven Models for Groundwater Level Forecasting

Water Resources Management - Irregular rainfall patterns and limited freshwater availability have driven humans to increase their dependence on groundwater resources. An essential aspect of...     

Development of Nonlinear Model Based on Wavelet-ANFIS for Rainfall Forecasting at Klang Gates Dam

Rainfall is one of the most complicated effective hydrologic processes in runoff prediction and water management. Artificial neural networks (ANN) have been found efficient, particularly in problems where characteristics of the processes are stochastic and difficult to describe using explicit mathematical models. However, time series prediction based on ANN algorithms is fundamentally difficult and faces some other problems. For this purpose, one method that has been identified as a possible alternative for ANN in hydrology and water resources problems is the adaptive neuro-fuzzy inference system (ANFIS). Nevertheless, the data arising from the monitoring stations and experiment might be corrupted by noise signals owing to systematic and non-systematic errors. This noisy data often made the prediction task relatively difficult. Thus, in order to compensate for this augmented noise, the primary objective of this paper is to develop a technique that could enhance the accuracy of rainfall prediction. Therefore, the wavelet decomposition method is proposed to link to ANFIS and ANN models. In this paper, two scenarios are employed; in the first scenario, monthly rainfall value is imposed solely as an input in different time delays from the time (t) to the time (t-4) into ANN and ANFIS, second scenario uses the wavelet transform to eliminate the error and prepares sub-series as inputs in different time delays to the ANN and ANFIS. The four criteria as Root Mean Square Error (RMSE), Correlation Coefficient (R ²), Gamma coefficient (G), and Spearman Correlation Coefficient (ρ) are used to evaluate the proposed models. The results showed that the model based on wavelet decomposition conjoined with ANFIS could perform better than the ANN and ANFIS models individually.     

RD预报模型在渠井结合灌区地下水位动态预测中的运用

陕西省泾惠渠灌区是一个典型的渠井结合灌区。以Visual Modflow地下水数值模拟软件为开发平台,在建立的灌区地下水数值模型的基础上,利用时间序列及神经网络等理论,建立了基于随机——确定(random-determination简称RD)耦合方法的地下水预报模型,并对灌区未来10年的地下水位动态进行了分析和预测,以指导灌区未来地下水资源可持续开发利用。     

Hybrid SSA-ARIMA-ANN Model for Forecasting Daily Rainfall

Rainfall, which is one of the most important hydrologic processes, is influenced by many meteorological factors like climatic change, atmospheric temperature, and atmospheric pressure. Even though there are several stochastic and data driven hydrologic models, accurate forecasting of rainfall, especially smaller time step rainfall forecasting, still remains a challenging task. Effective modelling of rainfall is puzzling due to its inherent erratic nature. This calls for an efficient model for accurately forecasting daily rainfall. Singular Spectrum Analysis (SSA) is a time series analysis tool, which is found to be a very successful data pre-processing algorithm. SSA decomposes a given time series into a finite number of simpler and decipherable components. This study proposes integration of Singular Spectrum Analysis (SSA), Auto Regressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) into a hybrid model (SSA-ARIMA-ANN), which can yield reliable daily rainfall forecasts in a river catchment. In the present study, spatially averaged daily rainfall data over Koyna catchment, Maharashtra has been used. In this study SSA is proposed as a data pre-processing tool to separate stationary and non-stationary components from the rainfall data. Correlogram and Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test has been used to validate the stationary and non-stationary components. In the developed hybrid model, the stationary components of rainfall data are modelled using ARIMA method and non-stationary components are modelled using ANN. The study of statistical performance of the model shows that the hybrid SSA-ARIMA-ANN model could forecast the daily rainfall of the catchment with reliable accuracy.

     

A Probabilistic Nonlinear Model for Forecasting Daily Water Level in Reservoir

Accurate prediction and monitoring of water level in reservoirs is an important task for the planning, designing, and construction of river-shore structures, and in taking decisions regarding irrigation management and domestic water supply. In this work, a novel probabilistic nonlinear approach based on a hybrid Bayesian network model with exponential residual correction has been proposed for prediction of reservoir water level on daily basis. The proposed approach has been implemented for forecasting daily water levels of Mayurakshi reservoir (Jharkhand, India), using a historic data set of 22 years. A comparative study has also been carried out with linear model (ARIMA) and nonlinear approaches (ANN, standard Bayesian network (BN)) in terms of various performance measures. The proposed approach is comparable with the observed values on every aspect of prediction, and can be applied in case of scarce data, particularly when forcing parameters such as precipitation and other meteorological data are not available.     

A Comparative Study of Artificial Intelligence Models and A Statistical Method for Groundwater Level Prediction

Today, various methods have been developed to extract drinking water resources, which scientists use to simulate the quantitative and qualitative water resources parameters. Due to Iran's geographical and climatic characteristics, this region is located on the drought belt in Asia. In this research, some Artificial Intelligence (AI) and mathematical models have been used for groundwater level prediction. The AI models used for this research are Extreme Learning Machine (ELM), Least Square Support Vector Machine (LSSVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Multiple Linear Regression (MLR) model. In this study, simultaneously, these models were used to simulate and estimate groundwater level (GWL). The database used in the simulation is the data related to the Total Dissolved Solids (TDS), Electrical Conductivity (EC), Salinity (S), and Time (t) parameters. The results showed that ELM was more accurate than other methods. In Uncertainty Wilson Score Method (UWSM) analysis, ELM had an Underestimation performance and was determined as the more precise model.

     

Post Audit Analysis of a Groundwater Level Prediction Model in Developed Semiconfined Aquifer System

This paper assesses groundwater recharge under conditions of long-term groundwater pumping at the Ravnik pumping site in Croatia and analyses the groundwater level prediction model used in prior aquifer modelling. The results of model calibration revealed a very low net infiltration rate at the start of the pumping site’s operation. As the operation continued, the net infiltration rate slowly increased, while the percentage of infiltrated rainfall scaled up with increasing pumping rates. The predicted recharge of the covering aquitard amounts approximately 14–15 % of the mean annual precipitation. The aquifer recharge takes place from aquitard by seepage. A subsequent simulation of the pumping site’s operation was performed for the 9 years period on the assumption that the pumping rates and the groundwater recharge would be the same as those recorded during the final calibration years. Results show that the post audit measured levels correspond relatively well to the predicted levels and that increasing of the pumping rate causes changes in the water budget in advantage of net groundwater recharge as a consequence of spreading recharge area outside of previous model boundaries.     

A Hybrid Wavelet and Neuro-Fuzzy Model for Forecasting the Monthly Streamflow Data

Researchers have studied to forecast the streamflow in order to develop the water usage policy. They have used not only traditional methods, but also computer aided methods. Some black-box models, like Adaptive Neuro Fuzzy Inference Systems (ANFIS), became very popular for the hydrologic engineering, because of their rapidity and less variation requirements. Wavelet Transform has become a useful tool for the analysis of the variations in time series. In this study, a hybrid model, Wavelet-Neuro Fuzzy (WNF), has been used to forecast the streamflow data of 5 Flow Observation Stations (FOS), which belong to Sakarya Basin in Turkey. In order to evaluate the accuracy performance of the model, Auto Regressive Integrated Moving Average (ARIMA) model has been used with the same data sets. The comparison has been made by Root Mean Squared Errors (RMSE) of the models. Results showed that hybrid WNF model forecasts the streamflow more accurately than ARIMA model.     

时间序列分析在地下水位预报中的应用

依据北京市地下水位观测井月平均水位资料,运用逐步自回归模型、指数平滑模型、季节性模型3种时间序列模型分别建立地下水位动态模拟和预测模型,并对模型的模拟和预测精度进行对比分析。通过应用实例分析反映,时间序列模型可较全面地反映地下水位动态变化规律,且计算简单,所需资料较少且易于获得,可以作为一种简易快速的地下水位模拟预测模型,能为地下水资源合理开发利用和科学管理提供参考依据。     

Processing Modflow在地下水位预测中的应用

数值模拟软件逐渐成为预测地下水演化更普遍的工具,并且广泛应用于地下水动态变化研究.以乌苏市平原区为例,结合区域水文地质条件及钻井资料,利用Processing Modflow建立三维水流数值模拟模型,并对该模型进行平面流场拟合,验证出模拟值基本符合2018年实测地下水位,通过模型模拟2018—2027年不同条件地下水位...     

一种区域地下水位预报的时间序列分析组合模型

根据地下水位动态变化的特征 ,建立提取水文趋势项的时间序列分析组合模型 ,并用逆函数法对地下水位进行预报。经典型实例验证 ,模型精度较高 ,具有一定的实用价值     

A Novel Approach for Prediction of Monthly Ground Water Level Using a Hybrid Wavelet and Non-Tuned Self-Adaptive Machine Learning Model

Water Resources Management - In recent decades, due to groundwater withdrawal in the Kabodarahang region, Iran, Hamadan, hazardous events such as sinkholes, droughts, water scarcity, etc., have...     

基于加权马尔可夫链的地下水动态预测

为了降低地下水变化中的季节性影响,对实测样本序列进行了分月标准化处理,以此地下水标准化指标为基础,考虑不同滞时的地下水埋深为相依随机变量的特点,用归一化的各阶自相关系数计算权重,应用加权马尔可夫链构建了地下水动态预测模型。以赤峰市中心城区2000年至2013年逐月地下水埋深实测序列为基础,实现了赤峰市中心城区预见期为1个月的地下水动态预测。结果表明:通过对原始时间序列进行分月标准化处理,有效降低了地下水变化过程中季节性的影响,使得以月为时间尺度的地下水预测成为可能;该模型依据不同滞时变量的相依关系计算权重,充分利用了原时间序列的信息;建模过程简单,所需资料较少,拟合精度较高,对于地下水预测具有较强的适用性。     

Groundwater Level Forecasting in a Shallow Aquifer Using Artificial Neural Network Approach

Forecasting the ground water level fluctuations is an important requirement for planning conjunctive use in any basin. This paper reports a research study that investigates the potential of artificial neural network technique in forecasting the groundwater level fluctuations in an unconfined coastal aquifer in India. The most appropriate set of input variables to the model are selected through a combination of domain knowledge and statistical analysis of the available data series. Several ANN models are developed that forecasts the water level of two observation wells. The results suggest that the model predictions are reasonably accurate as evaluated by various statistical indices. An input sensitivity analysis suggested that exclusion of antecedent values of the water level time series may not help the model to capture the recharge time for the aquifer and may result in poorer performance of the models. In general, the results suggest that the ANN models are able to forecast the water levels up to 4 months in advance reasonably well. Such forecasts may be useful in conjunctive use planning of groundwater and surface water in the coastal areas that help maintain the natural water table gradient to protect seawater intrusion or water logging condition.     

哈头才当水源地地下水位模拟与预测

采用MODFLOW软件对哈头才当水源地地下位进行数值模拟,经数值模型识别与验证,所取参数基本合理,水位拟合情况良好,模型能够真实地反映水源地地下水位的变化特征。在模型识别与验证的基础上,给出预测模型的初始条件、边界条件及其源汇项,对2009年10月—2029年10月地下水位进行了预测。结果表明:水源地按照设计开采方案开采,地下水位不会持续下降,计算区内大部分区域水位降深小于6 m,总体降深较小,不会对生态造成明显的影响。     

Artificial Neural Network Modeling for Groundwater Level Forecasting in a River Island of Eastern India

Forecasting of groundwater levels is very useful for planning integrated management of groundwater and surface water resources in a basin. In the present study, artificial neural network models have been developed for groundwater level forecasting in a river island of tropical humid region, eastern India. ANN modeling was carried out to predict groundwater levels 1 week ahead at 18 sites over the study area. The inputs to the ANN models consisted of weekly rainfall, pan evaporation, river stage, water level in the drain, pumping rate and groundwater level in the previous week, which led to 40 input nodes and 18 output nodes. Three different ANN training algorithms, viz., gradient descent with momentum and adaptive learning rate backpropagation (GDX) algorithm, Levenberg–Marquardt (LM) algorithm and Bayesian regularization (BR) algorithm were employed and their performance was evaluated. As the neural network became very large with 40 input nodes and 18 output nodes, the LM and BR algorithms took too much time to complete a single iteration. Consequently, the study area was divided into three clusters and the performance evaluation of the three ANN training algorithms was done separately for all the clusters. The performance of all the three ANN training algorithms in predicting groundwater levels over the study area was found to be almost equally good. However, the performance of the BR algorithm was found slightly superior to that of the GDX and LM algorithms. The ANN model trained with BR algorithm was further used for predicting groundwater levels 2, 3 and 4 weeks ahead in the tubewells of one cluster using the same inputs. It was found that though the accuracy of predicted groundwater levels generally decreases with an increase in the lead time, the predicted groundwater levels are reasonable for the larger lead times as well.     

基于遗传程序设计的水库年末水位预测模型

为了克服传统预测方法的不足，采用遗传程序设计，建立了多年调节水库年末消落水位预测模型。该模型利用演化计算自动寻找最优的模型结构，比传统方法具有较大的灵活性和智能性。通过对洪家渡多年调节水库的实例计算，不仅得出影响因子与年末消落水位的最佳函数式，而且用于拟合和预报的精度均满足要求，可以为水库调度提供一定的依据。