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...     

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.     

Groundwater Level Simulation Using Soft Computing Methods with Emphasis on Major Meteorological Components

Water Resources Management - Precise estimation of groundwater level (GWL) might be of great importance for attaining sustainable development goals and integrated water resources management....     

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 .

     

推行工程精细化管理全面提升工程管理整体水平

工程精细化管理,是在工程规范化管理实施过程中建立的目标细分、标准细分、责任细分、流程细分,实施精确计划、精确控制、精确考核的一种科学管理模式.工程精细化管理是提升工程管理水平的有效途径,决策者转变思想观念是推行工程精细化管理的必要前提.潘家口水库工程通过制定标准化管理实施细则,建立内部考核奖惩机制,全面推行工程精细化管...     

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).     

Management of High Groundwater Level Aquifer in the Taipei Basin

Groundwater pumpings have been prohibited by the government since 1970 due to the overexploitation and severe land subsidence in the Taipei Basin. Declining water levels were gradually recovered back. Nowadays, high groundwater levels are developed in the Taipei Basin. This may cause safety problems such as seepage of underground facility, and liquefaction by the strong earthquake jeopardizing millions of people’s lives and properties in the metropolitan area of Taipei. To reduce the associated risks, the study aims to formulate a management strategy to rationally reduce the groundwater level declining trend and sustainable utilization of groundwater resources in the Taipei Basin. A hydrogeologic model of Taipei Basin using MODFLOW-96 was setup to evaluate water budget and safe yield of the aquifer. The simulated water budget indicates that the groundwater annual storage increases about 17 million cubic meters in the main (Jingmei) aquifer. The average groundwater safe yield of the Taipei Basin estimated by the Hill method is about 54 million cubic meters per year. Moreover, with consideration of the reduction of liquefaction risks the revised average safe yield is about 126 million cubic meters per year. To effectively use and manage groundwater resources, restriction order on the use of groundwater resources in the Taipei Basin need to be revised. The implementation of groundwater management index coupled with an upper limit of the average groundwater level set as −7.5 m below the surface for avoiding earthquake caused liquefaction is suggested to manage the groundwater level for safe-use of groundwater resources in the Taipei Basin.     

Precipitation Forecasting Using Wavelet-Genetic Programming and Wavelet-Neuro-Fuzzy Conjunction Models

Forecasting precipitation as a major component of the hydrological cycle is of primary importance in water resources engineering, planning and management as well as in scheduling irrigation practices. In the present study the abilities of hybrid wavelet-genetic programming [i.e. wavelet-gene-expression programming, WGEP] and wavelet-neuro-fuzzy (WNF) models for daily precipitation forecasting are investigated. In the first step, the single genetic programming (GEP) and neuro-fuzzy (NF) models are applied to forecast daily precipitation amounts based on previously recorded values, but the results are very weak. In the next step the hybrid WGEP and WNF models are used by introducing the wavelet coefficients as GEP and NF inputs, but no satisfactory results are produced, even though the accuracies increased to a great extent. In the third step, the new WGEP and WNF models are built; by merging the best single and hybrid models’ inputs and introducing them as the models inputs. The results show the new hybrid WGEP models are effective in forecasting daily precipitation, while the new WNF models are unable to learn the non linear process of precipitation very well.     

On Predictability of Groundwater Level in Shallow Wells Using Satellite Observations

Management of groundwater resources needs continuous and efficient monitoring networks. Sparsity of in situ measurements both spatially and temporally creates hindrance in framing groundwater management policies. Remotely sensed data can be a possible alternative. GRACE satellites can trace groundwater changes globally. Moreover, gridded rainfall (RF) and soil moisture (SM) data can shed some light on the hydrologic system. The present study attempts to use GRACE, RF and SM data at a local scale to predict groundwater level. Ground referencing of satellite data were done by using three machine learning techniques- Support Vector Regression (SVR), Random Forest Method (RFM) and Gradient Boosting Mechanism (GBM). The performance of the developed methodology was tested on a part of the Indo-Gangetic basin. The analyses were carried out for nine GRACE pixels to identify relationship between individual well measurements and satellite-derived data. These nine pixels are classified on the basis of presence or absence of hydrological features. Pixels with the presence of perennial streams showed reasonably good results. However, pixels with wells located mostly near the stream gave relatively poorer predictions. These results help in identifying wells which can reasonably represent the regional shallow groundwater dynamics.     

Application of Non-Linear Simulation and Optimisation Models in Groundwater Aquifer Management

The need for rational and overall water resources management has become, during the past decades, a problem ofmajor importance due to the rising water demands. In this paper atechnique is presented through which a management model that combines the useof two separate models, a flow simulation and an optimisation one, isused for groundwater management. The necessary stages for the formulationand the combined use of the two models, along with a number of problemsthat might arise during the development of the management model are alsopresented. This technique is applied to a large-scale case study problemthat forms an optimisation approach with a large number of non-linear decisionvariables. The results of the application of the management modeldemonstrate the importance of the use of such models both in managing rationallyavailable water resources and in reducing the operational cost of theirexploitation.     

Forecasting the Level of Reservoirs Using Multiple Input Fuzzification in ANFIS

Estimation the Level of water is one of the crucial subjects in reservoir management influencing on reservoir operation and decision making. One of the most accurate artificial intelligence model used broadly in water resource aspects is adaptive neuro-fuzzy interface system (ANFIS) taking in to account the membership functions (MF) on the basis of the smoothness characteristics and mathematical components each for set of input data. All researches in hydrological estimation used ANFIS, merely a type of MF has been noticed for all sets of inputs without considering the response of each of them. This study is applying a specified certain MFs for each type of input to improve the accuracy of ANFIS model in forecasting the water level in Klang Gates Dam in Malaysia. On the basis of the previous studies, two most popular MFs, Generalized Bell Shape MF and, Gaussian MF, are employed for examine the new pattern in two inputs ANFIS architecture resulted less stress in error performance, and higher accuracy in estimation, compare to the traditional ANFIS model. The aim is achieved by evaluating the performance in and fitness of the model in daily reservoir estimation.     

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.     

An Integrated Groundwater Management Mode Based on Control Indexes of Groundwater Quantity and Level

Groundwater is an important source of freshwater throughout the world. Due to over-exploitation of groundwater over many years, a number of potential adverse hydrogeological problems have raised. To reduce such adverse effects, it is necessary to carry out strict groundwater management in over-exploited areas. In this study, quantity-level binary control management mode has been developed in Tianjin. Initially, the management is the key to determine control levels of groundwater including the blue line levels (proper levels) and red line levels (warning levels), the blue line levels can be determined by the ground settlement recovery scenario, and the red line levels can be determined through planning groundwater exploitation scenarios. By comparing the real-time observed groundwater data with the blue levels and red levels the management grade of groundwater levels which are present, can thus be identified. Secondly, the corresponding management strategies would be determined by the management grade. On this basis reasonable groundwater levels and mining schemes can be made. Finally, the water quota for each sector can be optimized and adjusted in real time according to the binary groundwater management methodology established in this study. Thus, the exploitation of groundwater can be monitored and dynamically managed by the real-time monitoring levels and the sustainable utilization of groundwater resources can be achieved. To achieve all the objectives mentioned above, it is necessary to provide a powerful tool through the utilization of a numerical model for groundwater management. Based on geological and hydrogeological conditions in Tianjin city, a three-dimensional numerical groundwater flow model was established by coupling a one-dimensional soil consolidation model with MODFLOW model. Through calibration and verification, the model showed good simulation accuracy. It proved that the new management mode can provide a scientific basis for groundwater management.     

Intermittent Streamflow Forecasting by Using Several Data Driven Techniques

Forecasting intermittent streamflows is an important issue for water quality management, water supplies, hydropower and irrigation systems. This paper compares the accuracy of several data driven techniques, that is, adaptive neuro fuzzy inference system (ANFIS), artificial neural networks (ANNs) and support vector machine (SVM) for forecasting daily intermittent streamflows. The results are also compared with those of the local linear regression (LLR) and the dynamic local linear regression (DLLR). Intermittent streamflow data from two stations, Uzunkopru and Babaeski, in Thrace region located in north-western Turkey are used in the study. The root mean square error and correlation coefficient were used as comparison criteria. The comparison results indicated that the ANFIS, ANN and SVM models performed better than the LLR and DLLR models in forecasting daily intermittent streamflows. The ANN and ANFIS gave the best forecasts for the Uzunkopru and Babaeski stations, respectively.     

River Stage Forecasting Using Wavelet Packet Decomposition and Machine Learning Models

Water Resources Management - This study develops and applies three hybrid models, including wavelet packet-artificial neural network (WPANN), wavelet packet-adaptive neuro-fuzzy inference system...     

Analysis of Meteorological Drought Resilience and Risk Assessment of Groundwater Using Signal Analysis Method

Water Resources Management - Groundwater plays an important role in mitigating drought. It is necessary to analyze the spatiotemporal variation characteristics of groundwater and establish an...     

The Correlation Between Statistically Downscaled Precipitation Data and Groundwater Level Records in North-Western Turkey

Downscaling of atmospheric climate parameters is a sophisticated tool to develop statistical relationships between large-scale atmospheric variables and local-scale meteorological variables. In this study, the variables selected from the National Centre for Environmental Prediction and National Centre for Atmospheric Research (NCEP/NCAR) reanalysis data set were used as predictors for the downscaling of monthly precipitation in a watershed located in north-western Turkey where station records terminated two decades ago. An Artificial Neural Network (ANN) based approach was used to downscale global climate predictors that are positively correlated to the existing time frame of precipitation data in the basin. The downscaled precipitation information were used to extend the non-existing data from the meteorological station, which were later correlated with groundwater level data obtained from automatic pressure transducers that continuously record depth to groundwater. The results of the study showed that, among a large set of NCEP/NCAR parameters, surface precipitation data recorded at the meteorological station was strongly correlated with precipitation rate, air temperature and relative humidity at surface and air temperature at 850, 500, and 200 hPa pressure levels, and geopotential heights at 850 and 200 hPa pressure levels. The gaps in station data were then filled with the correlations obtained from NCEP/NCAR parameters and a complete precipitation data set was obtained that extended to current time line. This extended precipitation time series was later correlated with the existing groundwater level data from an alluvial plain in order to develop a general relationship that can be used in basin-wide water budget estimations. The proposed methodology is believed to serve the needs of engineers and basin planners who try to create a link between related hydrological variables under data-limited conditions.     

Forecasting High-Frequency River Level Series Using Double Switching Regression with ARMA Errors

River level forecasting is a difficult problem. Complex river dynamics lead to level series with strong time-varying serial correlation and nonlinear relations with influential factors. The current high-frequency level series present a new challenge: they are measured hourly or at finer time scales, but predictions of up to several days ahead are still needed. In this framework, prediction models must be able to provide h-step predictions for high h values. This work presents a new nonlinear model, double switching regression with ARMA errors, that addresses the features of level series. It distinguishes different regimes both in the regression and in the error terms of the model to capture time-varying correlations and nonlinear relations between response and predictors. The use of different regression and ARMA regimes will provide good h-step prediction for both low and high h values. We also propose a new estimation method that, in contrast to other switching models, does not need to define the regimes before estimating the model. This method is based on a two-step estimation and model-based recursive partitioning. The approach is applied to model the hourly levels of the Ebro River in Zaragoza (Spain), using as input an upstream location, Tudela. Using the fitted model, we obtain hourly predictions and confidence intervals up to three days ahead, with very good results. The model outperforms previous approaches, especially with high values and in cases of long-term predictions.