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.     

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.     

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).     

Application of Several Data-Driven Techniques for Predicting Groundwater Level

In this study, several data-driven techniques including system identification, time series, and adaptive neuro-fuzzy inference system (ANFIS) models were applied to predict groundwater level for different forecasting period. The results showed that ANFIS models out-perform both time series and system identification models. ANFIS model in which preprocessed data using fuzzy interface system is used as input for artificial neural network (ANN) can cope with non-linear nature of time series so it can perform better than others. It was also demonstrated that all above mentioned approaches could model groundwater level for 1 and 2 months ahead appropriately but for 3 months ahead the performance of the models was not satisfactory.     

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 .

     

Waxing and waning slacks: The changing ecohydrology of interdunal wetlands/slacks in a Lake Michigan coastal dune complex during rising Lake Michigan-Huron levels

Since 2016 we have studied the largest interdunal wetlands/slack lying within a deflated parabolic dune east of Lake Michigan. Geologic cross-sections show ∼ 15 m of sand and gravel beneath the dunes, creating an aquifer hydraulically connecting Lake Michigan-Huron (MH) with the water table/shallow groundwater influencing the slack. Lake Michigan-Huron (MH) water levels have risen ∼ 1 m from 2016 to 2020, increasing water levels within and around the slack ∼ 1 m. Color-infrared images and vegetation quadrat sampling show water appearing, then significantly expanding with the main slack and upland/dune vegetation transitioning to wetland vegetation in response to this rise. Monitoring well data show slack water levels rise in spring as Lake MH rises. Levels drop as the growing season begins while Lake MH continues to rise through summer. Short-term slack water level increases occur due to local rain events, but significant water level declines follow due to evapotranspiration. Slack water levels begin to rise again in late summer and into fall as the end of the growing season arrives, evapotranspiration decreases, and heavier, more frequent rain events occur. Together, these factors push slack water levels to their highest point of the year while Lake MH levels are decreasing. In late fall–winter, slack water levels drop in concert with Lake MH levels. Climate change effects, increased transpiration from higher temperatures, summer drought, and greater variability in lake level fluctuations, may make it more difficult to maintain wet growing conditions for hydrophytic vegetation. Hence, climate change poses risks to the existence of this imperiled ecosystem.     

Wavelet Regression Model as an Alternative to Neural Networks for River Stage Forecasting

River stage forecasting is an important issue in water resources management and real-time prediction of extreme floods. The present study investigates the performance of the wavelet regression (WR) technique in daily river stage forecasting. The WR model was improved combining two methods, discrete wavelet transform and a linear regression model. Two different WR models were developed using the stage sub-time series, and these were compared with each other. The data from two stations on the Schuylkill River in Philadelphia were used. The root mean square errors (RMSE), mean absolute errors (MAE) and correlation coefficient (R) statistics were used for evaluating the accuracy of the WR models. The accuracy of the WR models was then compared with those of the artificial neural networks (ANN) models. Based on a comparison of these results, the WR models were found to perform better than the ANN models. For the upstream and downstream stations, it was found that the WR models with upstream readings of with RMSE = 0.070, MAE = 0.027, R = 0.937 and with downstream readings of RMSE = 0.048, MAE = 0.024, R = 0.969 in the validation stage performed better in forecasting daily river stages than the best accurate ANN models with upstream readings of RMSE = 0.168, MAE = 0.052, R = 0.802 and with downstream readings of RMSE = 0.115, MAE = 0.051, R = 0.807, respectively.     

Assessing surface water–groundwater interactions in a complex river‐floodplain wetland‐isolated lake system

Floodplain systems are most often hydrologically complex settings characterized by highly variable surface water–groundwater interactions that are subjected to wide‐ranging wetting and drying over seasonal timeframes. This study used field methods, statistical analysis, and the Darcy's law approach to explore surface water–groundwater dynamics, interactions, and fluxes in a geographically complex river‐floodplain wetland‐isolated lake system (Poyang Lake, China). The floodplain system of Poyang Lake is affected by strongly seasonal shifts between dry and wet processes that cause marked changes in surface water and groundwater flow regimes. Results indicate that wetland groundwater is more sensitive to variations in river levels than the seasonal isolated lakes. In general, groundwater levels are lower than those of the isolated lakes but slightly higher than river levels. Statistical analysis indicates that the river hydrology plays a more significant role than the isolated lakes in controlling floodplain groundwater dynamics. Overall, the river shows gaining conditions and occasionally losing conditions with highly variable Darcy fluxes of up to +0.4 and ?0.2 m/day, respectively, whereas the isolated lakes are more likely to show slightly losing conditions (less than ?0.1 m/day). Although seasonal flux rates range from 7.5 to 48.2 m/day for surface water–groundwater interactions in the floodplain, the flux rates for river–groundwater interactions were around four to seven times higher than that of the isolated lake–groundwater interactions. The outcomes of this study have important implications for improving the understanding of the water resources, water quality, and ecosystem functioning for both the river and the lake.     

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.     

Drought Forecasting Using Neural Networks,Wavelet Neural Networks,and Stochastic Models: Case of the Algerois Basin in North Algeria

Drought forecasting is a major component of a drought preparedness and mitigation plan. This paper focuses on an investigation of artificial neural networks (ANN) models for drought forecasting in the algerois basin in Algeria in comparison with traditional stochastic models (ARIMA and SARIMA models). A wavelet pre-processing of input data (wavelet neural networks WANN) was used to improve the accuracy of ANN models for drought forecasting. The standard precipitation index (SPI), at three time scales (SPI-3, SPI-6 and SPI-12), was used as drought quantifying parameter for its multiple advantages. A number of different ANN and WANN models for all SPI have been tested. Moreover, the performance of WANN models was investigated using several mother wavelets including Haar wavelet (db1) and 16 daubechies wavelets (dbn, n varying between 2 and 17). The forecast results of all models were compared using three performance measures (NSE, RMSE and MAE). A comparison has been done between observed data and predictions, the results of this study indicate that the coupled wavelet neural network (WANN) models were the best models for drought forecasting for all SPI time series and over lead times varying between 1 and 6 months. The structure of the model was simplified in the WANN models, which makes them very convenient and parsimonious. The final forecasting models can be utilized for drought early warning.

     

Improved Water Level Forecasting Performance by Using Optimal Steepness Coefficients in an Artificial Neural Network

Developing water level forecasting models is essential in water resources management and flood prediction. Accurate water level forecasting helps achieve efficient and optimum use of water resources and minimize flooding damages. The artificial neural network (ANN) is a computing model that has been successfully tested in many forecasting studies, including river flow. Improving the ANN computational approach could help produce accurate forecasting results. Most studies conducted to date have used a sigmoid function in a multi-layer perceptron neural network as the basis of the ANN; however, they have not considered the effect of sigmoid steepness on the forecasting results. In this study, the effectiveness of the steepness coefficient (SC) in the sigmoid function of an ANN model designed to test the accuracy of 1-day water level forecasts was investigated. The performance of data training and data validation were evaluated using the statistical index efficiency coefficient and root mean square error. The weight initialization was fixed at 0.5 in the ANN so that even comparisons could be made between models. Three hundred rounds of data training were conducted using five ANN architectures, six datasets and 10 steepness coefficients. The results showed that the optimal SC improved the forecasting accuracy of the ANN data training and data validation when compared with the standard SC. Importantly, the performance of ANN data training improved significantly with utilization of the optimal SC.     

Groundwater Level Forecast Via a Discrete Space-State Modelling Approach as a Surrogate to Complex Groundwater Simulation Modelling

Reliable and precise forecasts of future groundwater level fluctuations are crucial constituents of sustainable management of scarce water resources and design of remediation plans. Groundwater simulations and predictions are often performed by employing physically based models, which are not applicable in a majority of water scarce areas around the globe, particularly in the developing countries like Bangladesh due to data limitations. On the other hand, data-driven statistical forecast models have demonstrated their suitability to model nonlinear and complex hydrogeological processes to forecast short- and long-term groundwater level fluctuations. The purpose of this effort is to propose a non-physical based approach by utilizing a discrete Space-State model as a prediction tool to forecast future scenarios of groundwater level fluctuations. The present study utilizes the prediction focused approach of the system identification process in which the overall objective is to develop a pragmatic dynamic system model. The performance of the proposed approach is evaluated for groundwater level data at three observation wells of Tanore upazilla in Rajshahi district, Bangladesh. Historical weekly time series data of groundwater level fluctuations from the three observation wells for 39 (1980–2018) years is used to develop the time series model, which is used for future groundwater level predictions for a period of next 22 years (up to 2040). The findings demonstrate the conceivable applicability of the proposed discrete Space-State modelling approach in forecasting future scenarios of groundwater level fluctuations in the selected observation wells.

     

Daily prediction of marine beach water quality in Hong Kong

Bacterial concentration (Escherichia coli) is generally adopted as a key indicator of beach water quality. Currently the beach management system in Hong Kong relies on past water quality data sampled at intervals between 3 and 14 days. Beach advisories are issued when the geometric mean E. coli level of the past five samples exceeds the beach water quality objective (WQO) of 180 counts/100 mL. When the E. coli level varies dynamically, the system is not able to track the daily bacterial variation. And yet worldwide there does not exist a generally accepted method to predict beach water quality in a marine environment, which is influenced by hydro-meteorological variables, catchment characteristics, as well as complicated tidal currents and wave effects.A comprehensive study of beach water quality prediction has been carried out for four representative beaches in Hong Kong: Big Wave Bay (BW), Deep Water Bay (DW), New Cafeteria (NC) and Silvermine Bay (SIL). Statistical analysis of the extensive regular monitoring data was carried out for two periods before and after the commissioning of the Harbour Area Treatment Scheme (HATS): (1990–1997) and (2002–2006) respectively. The data analysis shows that E. coli is strongly correlated with seven hydro-environmental variables: rainfall, solar radiation, wind speed, tide level, salinity, water temperature and past E. coli concentration. The relative importance of the parameters is beach-specific, and depends on the local geographical and hydrographical characteristics as well as location of nearby pollution sources.Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models are developed from the sparsely sampled regular monitoring data (2002–2006) to predict the next-day E. coli concentration using the key hydro-environmental variables as input parameters. The models are validated against daily monitoring data in the bathing seasons of 2007 and 2008. The models are able to track the dynamic changes in E. coli concentration and predict WQO compliance/exceedance with an overall accuracy of 70–96%. Both the MLR and ANN models are superior to the current beach advisories in capturing water quality variations, and in predicting WQO exceedances. For example, the models predict around 80% and 50% of the exceedances at BW and NC respectively in June–July 2007, as compared to 0% and 14% based purely on past data. Similarly, observed exceedances are predicted with success rates of 71%, 42%, and 53% at BW, NC, and SIL respectively during July–October 2008, as compared with 0%, 0%, and 6% using the current water quality assessment criterion. The MLR and ANN models have similar performances; ANN model tends to be better in predicting the high-end concentrations, with however a greater number of false positive predictions (false alarms).This work demonstrates the practical feasibility of predicting bacterial concentration based on the critical hydro-environmental variables, and paves the way for developing a real time water quality forecast and management system for Hong Kong.     

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.     

Assessment of Sustainable Yield of Karst Water in Huaibei,China

This paper presents the assessment of sustainable yield in the Huaibei karst water area of Anhui province, China. A review of sustainable yield definition is introduced first in this paper, and sustainable development in karst areas is more difficult due to the complicated hydrogeologic conditions. General hydrogeology of the study area is provided to characterize hydraulic connections between the karst aquifer and an overlying porous aquifer. Groundwater level declines continuously due to over-exploitation of the karst groundwater, and two layers of groundwater dropping funnel were formed in Huaibei. These problems not only threaten the eco-geo-environment, but also compromise the water utilization which depends on the shallow porous water. A “critical water level” is proposed in this study to assess the sustainable yield, and it is determined by the historical exploitation data which represent the relationship between the karst water and the shallow porous water uses. A three layer Artificial Neural Network (ANN) model is used to understand the complex relationship of the karst water level and its influencing factors. Precipitation, exploitation and water level of latest period are chosen as the input nodes, seasonal records of water level are simulated by the ANN model. The sustainable yield is calculated by the trail-and-error adjusting method, and is equal to the pumping rate when the “critical water level” is maintained. The rate of 30.05 MCM/a is the sustainable yield for the Huaibei karst area in 2008, and it is less than the real pumping rate of 35.92 MCM/a. This assessment is meaningful to the management for the Huaibei karst water.     

Applying Genetic Algorithm and Neural Network to the Conjunctive Use of Surface and Subsurface Water

The conjunctive use of surface and subsurface water is one of the most effective ways to increase water supply reliability with minimal cost and environmental impact. This study presents a novel stepwise optimization model for optimizing the conjunctive use of surface and subsurface water resource management. At each time step, the proposed model decomposes the nonlinear conjunctive use problem into a linear surface water allocation sub-problem and a nonlinear groundwater simulation sub-problem. Instead of using a nonlinear algorithm to solve the entire problem, this decomposition approach integrates a linear algorithm with greater computational efficiency. Specifically, this study proposes a hybrid approach consisting of Genetic Algorithm (GA), Artificial Neural Network (ANN), and Linear Programming (LP) to solve the decomposed two-level problem. The top level uses GA to determine the optimal pumping rates and link the lower level sub-problem, while LP determines the optimal surface water allocation, and ANN performs the groundwater simulation. Because the optimization computation requires many groundwater simulations, the ANN instead of traditional numerical simulation greatly reduces the computational burden. The high computing performance of both LP and ANN significantly increase the computational efficiency of entire model. This study examines four case studies to determine the supply efficiencies under different operation models. Unlike the high interaction between climate conditions and surface water resource, groundwater resources are more stable than the surface water resources for water supply. First, results indicate that adding an groundwater system whose supply productivity is just 8.67 % of the entire water requirement with a surface water supply first (SWSF) policy can significantly decrease the shortage index (SI) from 2.93 to 1.54. Second, the proposed model provides a more efficient conjunctive use policy than the SWSF policy, achieving further decrease from 1.54 to 1.13 or 0.79, depending on the groundwater rule curves. Finally, because of the usage of the hybrid framework, GA, LP, and ANN, the computational efficiency of proposed model is higher than other models with a purebred architecture or traditional groundwater numerical simulations. Therefore, the proposed model can be used to solve complicated large field problems. The proposed model is a valuable tool for conjunctive use operation planning.     

Impact of Flood Spreading on Groundwater Level Variation and Groundwater Quality in an Arid Environment

In arid and semiarid areas, bimodal and high rainfall leads to infrequent flood that can be extremely damaging. To reduce the impacts of persistent intra-seasonal drought and also to reduce flood damaging in arid and semiarid areas, rainwater storage is a prerequisite that keeps water far from evapotranspiration, increases groundwater level and decreases flood hazards modification to exchange between surface water and groundwater through flood spreading, dams, etc. The purpose of this paper is to delineate and explain variations in groundwater recharge and groundwater quality along an ephemeral stream that has been modified by flood spreading. Groundwater samples were collected from 14 deep wells located at different distances from flood spreading projection area (FSPA) in 1 month interval during September 2005 to September 2008. Groundwater quality was followed via Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl^-, Hco₃^- SO₄^2-, Electrical Conductivity (EC) and pH measurements for two time periods between 2005 and 2008. The results show significant impact of flood spreading in groundwater table and groundwater salinity variation. Groundwater table decreased in all study wells, but groundwater drawdown increased by increasing the distance to FSPA (during 4 years study, 11.02 m in the well located at 20 m of FSPA versus 38.88 in the well located at 1,825 m). Also ion concentration increased in all of the wells during the study period, but the increasing ion concentration was significantly less important in FSPA closeness.     

Forecasting Urban Water Demand Via Wavelet-Denoising and Neural Network Models. Case Study: City of Syracuse, Italy

Forecasting urban water demand can be of use in the management of water utilities. For example, activities such as water-budgeting, operation and maintenance of pumps, wells, reservoirs, and mains require quantitative estimations of water resources at specified future dates. In this study, we tackle the problem of forecasting urban water demand by means of back-propagation artificial neural networks (ANNs) coupled with wavelet-denoising. In addition, non-coupled ANN and Linear Multiple Regression were used as comparison models. We considered the case of the municipality of Syracuse, Italy; for this purpose, we used a 7?year-long time series of water demand without additional predictors. Six forecasting horizons were considered, from 1 to 6?months ahead. The main objective was to implement a forecasting model that may be readily used for municipal water budgeting. An additional objective was to explore the impact of wavelet-denoising on ANN generalization. For this purpose, we measured the impact of five different wavelet filter-banks (namely, Haar and Daubechies of type db2, db3, db4, and db5) on a single neural network. Empirical results show that neural networks coupled with Haar and Daubechies?? filter-banks of type db2 and db3 outperformed all of the following: non-coupled ANN, Multiple Linear Regression and ANN models coupled with Daubechies filters of type db4 and db5. The results of this study suggest that reduced variance in the training-set (by means of denoising) may improve forecasting accuracy; on the other hand, an oversimplification of the input-matrix may deteriorate forecasting accuracy and induce network instability.     

Application of Artificial Neural Networks for Identifying Optimal Groundwater Pumping and Piping Network Layout

The simulation-optimization approach is often used to solve water resource management problem although repeated use of the simulation model enhances the computational load. In this study, Artificial Neural Network (ANN) and Bagged Decision Trees (BDT) models were developed as an approximator for Analytic Element Method (AEM) based groundwater flow model. Developed ANN and BDT models were coupled with Particle Swarm Optimization (PSO) model to solve the well-field management problem. The groundwater flow model was developed for the study area and used to generate the dataset for the training and testing of the ANN & BDT models. These coupled ANN-PSO & BDT-PSO models were employed to find the optimal design and cost of the new well-field system by optimizing discharge & co-ordinate of wells along with the cost effective layout of piping network. The Minimum Spanning Tree (MST) based model was used to find out the optimal piping network layout and checking the hydraulic constraints in the piping network. The results show that the ANN & BDT models are good approximators of AEM model and they can reduce the computational burden significantly although ANN model performs better than BDT model. The results show that the coupling of piping network model with simulation-optimization model is very significant for finding the cost effective and realistic design of the new well-field system.     

Comparison of performance of statistical models in forecasting monthly streamflow of Kizil River,China

This paper presents the application of autoregressive integrated moving average(ARIMA),seasonal ARIMA(SARIMA),and Jordan-Elman artificial neural networks(ANN)models in forecasting the monthly streamflow of the Kizil River in Xinjiang,China.Two different types of monthly streamflow data(original and deseasonalized data)were used to develop time series and Jordan-Elman ANN models using previous flow conditions as predictors.The one-month-ahead forecasting performances of all models for the testing period(1998-2005)were compared using the average monthly flow data from the Kalabeili gaging station on the Kizil River.The Jordan-Elman ANN models,using previous flow conditions as inputs,resulted in no significant improvement over time series models in one-month-ahead forecasting.The results suggest that the simple time series models(ARIMA and SARIMA)can be used in one-month-ahead streamflow forecasting at the study site with a simple and explicit model structure and a model performance similar to the Jordan-Elman ANN models.