A Wavelet-ANFIS Hybrid Model for Groundwater Level Forecasting for Different Prediction Periods

Artificial neural network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) have an extensive range of applications in water resources management. Wavelet transformation as a preprocessing approach can improve the ability of a forecasting model by capturing useful information on various resolution levels. The objective of this research is to compare several data-driven models for forecasting groundwater level for different prediction periods. In this study, a number of model structures for Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Wavelet-ANN and Wavelet-ANFIS models have been compared to evaluate their performances to forecast groundwater level with 1, 2, 3 and 4 months ahead under two case studies in two sub-basins. It was demonstrated that wavelet transform can improve accuracy of groundwater level forecasting. It has been also shown that the forecasts made by Wavelet-ANFIS models are more accurate than those by ANN, ANFIS and Wavelet-ANN models. This study confirms that the optimum number of neurons in the hidden layer cannot be always determined by using a specific formula but trial-and-error method. The decomposition level in wavelet transform should be determined according to the periodicity and seasonality of data series. The prediction of these models is more accurate for 1 and 2 months ahead (for example RMSE?=?0.12, E?=?0.93 and R ²?=?0.99 for wavelet-ANFIS model for 1 month ahead) than for 3 and 4 months ahead (for example RMSE?=?2.07, E?=?0.63 and R ²?=?0.91 for wavelet-ANFIS model for 4 months ahead).     

The Use of NARX Neural Networks to Forecast Daily Groundwater Levels

The lack of information to manage groundwater for irrigation is one of the biggest concerns for farmers and stakeholders in agricultural areas of Mississippi. In this study, we present a novel implementation of a nonlinear autoregressive with exogenous inputs (NARX) network to simulate daily groundwater levels at a local scale in the Mississippi River Valley Alluvial (MRVA) aquifer, located in the southeastern United States. The NARX network was trained using the Levenberg-Marquardt (LM) and Bayesian Regularization (BR) algorithms, and the results were compared to identify an optimal architecture for the forecasting of daily groundwater levels over time. The training algorithms were implemented using different hidden node combinations and delays (5, 25, 50, 75, and 100) until the optimal network was found. Eight years of daily historical input time series including precipitation and groundwater levels were used to forecast groundwater levels up to three months ahead. The comparison between LM and BR showed that NARX-BR is superior in forecasting daily levels based on the Mean Squared Error (MSE), coefficient of determination (R²), and Nash-Sutcliffe coefficient of efficiency. The results showed that BR with two hidden nodes and 100 time delays provided the most accurate prediction of groundwater levels with an error of ± 0.00119 m. This innovative study is the first of its kind and will provide significant contributions for the implementation of data-based models (DBMs) in the prediction and management of groundwater for agricultural use.     

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.     

河北地下水动态监测现状、规划和前景分析

地下水动态监测是地下水研究和地下水资源管理的重要手段,专业的监测网络是获取准确地下水动态信息的保障。结合对地下水监测工作的体会和多年应用地下水自动监测仪器的经验,总结了河北省地下水动态监测工作历程,介绍了监测网络现状,系统分析了监测工作存在的问题,给出了地下水动态专业监测网络构成和建设的实质性意见,并就监测工作广阔前景进行了分析,为将来地下水动态专业监测网络建设工程提供参考。     

Optimized River Stream-Flow Forecasting Model Utilizing High-Order Response Surface Method

Accurate and reliable stream-flow forecasting has a key role in water resources planning and management. Most recently, soft computing approaches have become progressively prevalent in modelling hydrological variables and most specifically stream-flows. This is due to their ability to capture the non-linearity and non-stationarity characteristics of the hydrological variables with minimum information requirements. Despite this, they present several challenges in the modelling architecture, as there is a need to establish a suitable pre-processing method for the stream-flow data and an appropriate optimization model has to be integrated in order re-adjust the weights and biases associated with the model structure. On top of that, artificial intelligent models require “trial and error” procedures in order to be properly tuned (number of hidden layers, number of neurons within the hidden layers and the type of the transfer function). However, soft computing approach experienced several problems while calibration such as over-fitting. In this research, the Response Surface Method (RSM) is improved based on high-order polynomial functions for forecasting the river stream-flow namely; High-Order Response Surface (HORS) method. Several higher orders have been examined, second, third, fourth and fifth polynomial functions in order to figure out the best fit that able to mimic the pattern of stream-flow. In order to demonstrate the effectiveness of the proposed model, monthly stream-flow time series data located in Aswan High Dam (AHD) has been examined. A detailed analysis of the overall statistical indicators revealed that the proposed method showed outstanding performance for monthly stream-flow forecasting at AHD. It could be concluded that the fifth order polynomial function outperforms the other orders of the polynomial functions especially with May model who achieved minimum MAE 0.12, NRMSE 0.07, MSE 0.03 and maximum SF and R² (0.97, 0.99) respectively.     

对地下水动态监测工作的思考

地下水动态监测,对于水量和水质评价,以及水资源的合理开发利用,有非常重要的意义。简要说明唐山市地下水动态监测工作的现状,并以唐山市地下水动态监测工作为例,阐述地下水动态监测工作中存在的主要问题,提出解决办法,以保证地下水动态监测工作的正常顺利进行。最后,从加强地下水管理、优化地下水动态监测站网、实现自动化地下水动态监测提出一定建议。     

Evaluation of suspended load transport rate using transport formulas and artificial neural network models （Case study： Chelchay Catchment）＊

Accurate estimation of sediment load or transport rate is very important to a wide range of water resources projects. This study was undertaken to determine the most appropriate model to predict suspended load in the Chelchay Watershed, northeast of Iran. In total, 59 data series were collected from four gravel bed-rivers and a sand bed river and two depth integrating suspended load samplers to evaluate nine suspended load formulas and feed forward backpropagation Artificial Neural Network (ANN) structures. Although the Chang formula with higher correlation coefficient (r = 0.69) and lower Root Mean Square Error (RMSE = 0.013) is the best suspended load predictor among the nine studied formulas, the ANN models significantly outperform traditional suspended load formulas and show their superior performance for all statistical parameters. Among different ANN structures two models including 4 inputs, 4 hidden and one output neurons, and 4 inputs, 4 and one hidden and one output neurons provide the best simulation with the RMSE values of 0.0009 and 0.001, respectively.     

大庆市地下水资源承载力评价及对策研究

以大庆市地下水资源为研究背景,分析了大庆市地下水资源开发利用中存在的问题,并从水资源、社会、经济、生态环境等各方面的关系入手,建立了一套水资源承载力评价指标体系,并用物元模型对其承载力进行合理、全面的综合评价,并提出合理使用水资源的有关对策,可为大庆市水资源的规划和管理提供参考依据。     

Evaluating the Performance of CHIRPS Satellite Rainfall Data for Streamflow Forecasting

Water Resources Management - Streamflow forecasting can offer valuable information for optimal management of water resources, flood mitigation, and drought warning. This research aims in evaluating...     

Predicting Water Level Fluctuations in Lake Michigan-Huron Using Wavelet-Expert System Methods

Understanding and forecasting water level fluctuations in Lake Michigan-Huron is important for a variety of water resource management operations such as flood control, local water supply management, shoreline maintenance, ecosystem sustainability, recreation, and economic development. In this study, wavelet transform, fuzzy logic and multilayer perceptron techniques are combined to obtain new approaches for forecasting lake level fluctuation. The wavelet approach is used to decompose water level time series into its spectral bands. Predictive models have been developed as stand-alone fuzzy logic, stand-alone multilayer perceptron combined wavelet-fuzzy and combined wavelet-multilayer perceptron models in order to forecast the water level fluctuations. The models were tested to predict the current water level (at t monthly time step) and lead times including t?+?3, t?+?6, t?+?9 and t?+?12 time steps from the water levels at two previous time steps (t???2 and t???1). In this study, the historic water level data was obtained from Lake Michigan-Huron for the period between 1855 and 2006. For the model development, monthly water level data was divided into two groups. The training group consists of the data for the first 101 years (from 1855 to 1955) with 1212 data points, which were, then, used to predict the water levels for remaining 51 years (from 1956 to 2006). The results reveal that all the four models can predict the water levels quite accurately. In comparison, the combined wavelet-fuzzy logic and combined wavelet-multilayer perceptron models outperformed the stand-alone fuzzy and multilayer perceptron models for lead times of 1, 3, 6, 9 and 12 months. This comparison was performed based on the root mean squared error (RMSE), the coefficient of efficiency (CE), the mean absolute deviation (MAD) and the skill score (SS) between observed data and prediction results.     

Comparative Study of Hybrid-Wavelet Artificial Intelligence Models for Monthly Groundwater Depth Forecasting in Extreme Arid Regions,Northwest China

Prediction of groundwater depth (GWD) is a critical task in water resources management. In this study, the practicability of predicting GWD for lead times of 1, 2 and 3 months for 3 observation wells in the Ejina Basin using the wavelet-artificial neural network (WA-ANN) and wavelet-support vector regression (WA-SVR) is demonstrated. Discrete wavelet transform was applied to decompose groundwater depth and meteorological inputs into approximations and detail with predictive features embedded in high frequency and low frequency. WA-ANN and WA-SVR relative of ANN and SVR were evaluated with coefficient of correlation (R), Nash-Sutcliffe efficiency (NS), mean absolute error (MAE), and root mean squared error (RMSE). Results showed that WA-ANN and WA-SVR have better performance than ANN and SVR models. WA-SVR yielded better results than WA-ANN model for 1, 2 and 3-month lead times. The study indicates that WA-SVR could be applied for groundwater forecasting under ecological water conveyance conditions.     

Designing a Hydrological Real-Time System for Surface Water and Groundwater in Denmark with Engagement of Stakeholders

Increasing attention on extreme hydrological events has created considerable demand for real-time information on surface water, groundwater and the unsaturated zone. In the present study, we describe how to convert a national water resources model (DK-model) covering the entire freshwater cycle in Denmark to real-time application. We have engaged stakeholders in the process of designing a hydrological real-time system. The participatory approach has been supported by a web-based questionnaire survey and a participatory workshop. A system prototype presented to the stakeholders simulates groundwater levels, streamflow and water content in the root zone with a lead time of 48 h. The active engagement of stakeholders has provided very valuable insights and feedbacks regarding how model and data should be combined in a real-time to best supporting water resources management.     

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.     

严格地下水资源管理的总体架构探讨

地下水资源管理是水资源管理的重要组成部分。阐述了地下水资源管理的涵义和内容,探讨了地下水资源管理总体架构,提出了当前和今后一段时间地下水资源管理的重点工作设想,以便加强和全面推进地下水资源管理工作,促进地下水资源管理规范化、科学化、系统化、信息化和民主化,实现地下水资源可持续利用。     

承压水漏斗地区地下水位时空分布预报的BP网络模型

依据水均衡原理，导出承压水漏斗区任意一点水位与其影响因素之间的复杂的非线性关系，在此基础上提出承压水漏斗水位时空分布预报的BP神经网络模型。该模型具有 分布参数模型的特征，且不需用到区域的水文地质参数。最后针对某实例进行了模型设计及预报分析，通过对部分观测井后期实测数据的训练，优选出双隐层的网络结构及其网络参数。随后用这些观测井的前3年数据进行了检验，并对其他观测井数据进行预报。计算表明，该模型对地下水位拟合与预报的合格率较高，可以获得研究区域某一时刻水位在空间上的分布。     

GIS技术在水文水资源领域的应用现状与展望

水文水资源领域引入GIS地理信息系统,为水文水资源管理、查询、分析和模拟提供了一条加快捷有效的途径。GIS地理信息系统在水文水资源领域的应用主要有五个方面:地表水资源及地下水资源的空间分布和调配;流域面雨量计算;地下水资源勘查;水文模拟及水情预报。GIS地理信息系统在水文水资源领域应用的主要发展趋势包括四个方面:加强应用标准和规范的研究和制定;建立具有水文水资源特色的地理空间数据库;GIS融合水文水资源专业模型;GIS软件向多维发展。     

Operational Prediction of Groundwater Fluctuation in South Florida using Sequence Based Markovian Stochastic Model

The ecosystem of South Florida is characterized by a vast wetland system, karst surficial hydrogeology, and extended coastal boundary. The ecosystem is poised under risks of: ecological failure due to increased fragmentation by urbanization; groundwater flow disruption because of sinkhole formation; and intrusion of oceanic water with decreasing water table head because of drought or over pumping. It was found important to synthesize the spatiotemporal state of the groundwater hydrology and also develop a forecasting model to support the intensive management and monitoring in place. In this study, an objective was set to develop a stochastic sequence model capable of forecasting groundwater levels on a monthly span at a daily time scale. The groundwater level simulation was conceptualized as a sequence of daily fluctuating states of magnitudes and patterns that has a defined probability of occurrence. The model setup involved representation of daily fluctuation magnitudes in ten states and pattern changes in three states. The sequential occurrence of states of magnitudes and patterns at each time step was used for estimation of the transitional probabilities and employed in a hidden Markov model frame work for ensemble generation and estimation of posterior probabilities. A realization was chosen based on the highest maximum likelihood ratio of 90% and smallest root mean square error of 0.05–0.12 m against the historical data. A monthly forecasting at daily time step was done dynamically incorporating observed data at each time step and revising prior and posterior probability estimation in the hidden Markov model formulation. A case study was conducted at three well sites, which are situated at three different hydrogeologic settings. The model not only reproduced annual groundwater fluctuation patterns but also forecasted preceding monthly fluctuations at maximum likelihood ratio above 90% and root mean square error below 0.15 m. A further study was recommended first to analyze break point parametric estimation for seasonal analysis, and secondly to integrate the approach in other hydrological models for the purpose of synthetic groundwater fluctuation generation.     

Lake Level Prediction using Feed Forward and Recurrent Neural Networks

The protection of high quality fresh water in times of global climate changes is of tremendous importance since it is the key factor of local demographic and economic development. One such fresh water source is Vrana Lake, located on the completely karstified Island of Cres in Croatia. Over the last few decades a severe and dangerous decrease of the lake level has been documented. In order to develop a reliable lake level prediction, the application of the artificial neural networks (ANN) was used for the first time. The paper proposes time-series forecasting models based on the monthly measurements of the lake level during the last 38 years, capable to predict 6 or 12 months ahead. In order to gain the best possible model performance, the forecasting models were built using two types of ANN: the Long-Short Term Memory (LSTM) recurrent neural network (RNN), and the feed forward neural network (FFNN). Instead of classic lagged data set, the proposed models were trained with the set of sequences with different length created from the time series data. The models were trained with the same set of the training parameters in order to establish the same conditions for the performance analysis. Based on root mean squared error (RMSE) and correlation coefficient (R) the performance analysis shown that both model types can achieve satisfactory results. The analysis also revealed that regardless of the model types, they outperform classic ANN models based on datasets with fixed number of features and one month the prediction period. Analysis also revealed that the proposed models outperform classic time series forecasting models based on ARIMA and other similar methods .

     

Artificial Neural Network (ANN) Based Modeling for Karstic Groundwater Level Simulation

A relatively new method of addressing different hydrological problems is the use of artificial neural networks (ANN). In groundwater management ANNs are usually used to predict the hydraulic head at a well location. ANNs can prove to be very useful because, unlike numerical groundwater models, they are very easy to implement in karstic regions without the need of explicit knowledge of the exact flow conduit geometry and they avoid the creation of extremely complex models in the rare cases when all the necessary information is available. With hydrological parameters like rainfall and temperature, as well as with hydrogeological parameters like pumping rates from nearby wells as input, the ANN applies a black box approach and yields the simulated hydraulic head. During the calibration process the network is trained using a set of available field data and its performance is evaluated with a different set. Available measured data from Edward??s aquifer in Texas, USA are used in this work to train and evaluate the proposed ANN. The Edwards Aquifer is a unique groundwater system and one of the most prolific artesian aquifers in the world. The present work focuses on simulation of hydraulic head change at an observation well in the area. The adopted ANN is a classic fully connected multilayer perceptron, with two hidden layers. All input parameters are directly or indirectly connected to the aquatic equilibrium and the ANN is treated as a sophisticated analogue to empirical models of the past. A correlation analysis of the measured data is used to determine the time lag between the current day and the day used for input of the measured rainfall levels. After the calibration process the testing data were used in order to check the ability of the ANN to interpolate or extrapolate in other regions, not used in the training procedure. The results show that there is a need for exact knowledge of pumping from each well in karstic aquifers as it is difficult to simulate the sudden drops and rises, which in this case can be more than 6 ft (approx. 2 m). That aside, the ANN is still a useful way to simulate karstic aquifers that are difficult to be simulated by numerical groundwater models.     

Groundwater Protection and Management Strategy in Jordan

Groundwater resources are essential in Jordan that require careful planning and management in order to sustain human socio-economic development and various ecosystems. However these vital resources are under the threat of degradation by both mismanagement and over-exploitation that leads to contamination and decline of water levels. A new by-law, which specifically addresses pollution prevention and protection of water resources used for domestic purposes through appropriate land use restriction and zoning, is currently under preparation in Jordan. This law (i.e., Groundwater Management Policy) addresses the management of groundwater resources including development, protection, management, and reducing abstraction for each renewable aquifer to the sustainable rate (i.e., safe yield). Groundwater vulnerability mapping and delineation of groundwater protection zones were implemented in different areas in Jordan in cooperation between the German Bundesanstalt für Geowissenschaften und Rohstoffe (BGR) company and Ministry of Water and Irrigation. This paper presents the status of groundwater resources in Jordan and their major issues. It attempts to discuss the groundwater vulnerability and protection strategy and the impacts of over-exploitation on the groundwater aquifers in an integrated water resources management perspective.