Prediction of Water-Level in the Urmia Lake Using the Extreme Learning Machine Approach

Predicting the dynamics of water-level in lakes plays a vital role in navigation, water resources planning and catchment management. In this paper, the Extreme Learning Machine (ELM) approach was used to predict the daily water-level in the Urmia Lake. Daily water-level data from the Urmia Lake in northwest of Iran were used to train, test and validate the employed models. Results showed that the ELM approach can accurately forecast the water-level in the Urmia Lake. Outcomes from the ELM model were also compared with those of genetic programming (GP) and artificial neural networks (ANNs). It was found that the ELM technique outperforms GP and ANN in predicting water-level in the Urmia Lake. It also can learn the relation between the water-level and its influential variables much faster than the GP and ANN. Overall, the results show that the ELM approach can be used to predict dynamics of water-level in lakes.     

Reconstruction of Hydrometeorological Data in Lake Urmia Basin by Frequency Domain Analysis Using Additive Decomposition

Water Resources Management - Frequency domain analysis using an additive decomposition method is proposed to reconstruct the missing hydrometeorological data of selected sites in Lake Urmia basin...     

Prediction of Water Level using Monthly Lagged Data in Lake Urmia,Iran

Water Resources Management - Prediction of water level fluctuations in lakes is a necessary task in hydrological and limnological studies. Lake Urmia, a hyper-saline lake in the North Western part...     

Prediction of Urmia Lake Water-Level Fluctuations by Using Analytical, Linear Statistic and Intelligent Methods

Undoubtedly, the most significant factor with wise decision making and designing hydrological structures along the lake coasts is an accurate model of lake level changes. This issue becomes more and more important as recent global climate changes have completely reformed the behavior of traditional lake level fluctuations. Subsequently, estimating lake levels becomes more important and at the same time more difficult. This paper deals with modeling lake level changes of Lake Urmia located in north-west of Iran, in terms of both simulator and predictor models. According to this, two traditional simulator models based on water budget are developed which benefit from most effective components on water budget namely precipitation, evaporation, inflow and the lake level antecedents, as model inputs. Most famous linear modeling tools, Autoregressive with exogenous input (ARX) and Box-Jenkins (BJ) models are employed with the same mentioned inputs for prediction purpose. In addition, two other methods that are, Multi-Layer Perceptron (MLP) neural network and also Local Linear Neuro-Fuzzy (LLNF) are applied to investigate capability of intelligent nonlinear methods for lake level changes prediction. All models performances are indicated using both graph and numerical illustrations and results are discussed. Comparative results reveal that the intelligent methods are superior to traditional models for modeling lake level behavior as complex hydrological phenomena.     

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.     

Modeling of Phytoplankton-Nutrient Dynamics in Saginaw Bay,Lake Huron

A multi-class, phytoplankton simulation model was developed and calibrated to an extensive set of field data acquired on Saginaw Bay, Lake Huron, during 1974. Phytoplankton biomass was partitioned into five functional groups: diatoms, greens, non-N₂-fixing blue-greens, N₂-fixing blue-greens, and “others”. Nutrients included in the model were phosphorus, nitrogen, and silicon. The model was applied to a single spatial segment encompassing the inner portion of Saginaw Bay.Process level analyses were conducted with the calibrated model to determine the relative importance of various factors affecting phytoplankton and nutrient dynamics. The concept of a single limiting factor for phytoplankton growth was found to be overly simplistic. Results indicated that temperature and light were relatively more growth rate limiting than nutrients on an annual average basis. However, as a consequence of nutrient depletion, nutrients became relatively more important at the times of peak phytoplankton crops. Nitrogen was found to be relatively more growth rate limiting than phosphorus to the total phytoplankton crop, although important differences occurred among the individual functional groups. At various times, and for various groups, all three nutrients were important in limiting either the rates of growth and/or the maximum sizes of the phytoplankton crops. Results were consistent with the hypothesis that while nitrogen and silicon were important in phytoplankton-nutrient dynamics, the supply of phosphorus would ultimately determine the size of the blue-green component of the total crop because N₂-fixing blue-greens do not have absolute requirements for dissolved available nitrogen or silicon. Results indicated that phosphorus requirements of spring and fall diatom crops were satisfied primarily by external loadings. Phosphorus requirements of summer blue-green crops were satisfied primarily by recycle processes within the water column. Upon cell death, direct nutrient recycle to the available nutrient compartments in the water column from excess internal phytoplankton stores was found to be important for both phosphorus and nitrogen. Phytoplankton production was found to be extremely sensitive to variations in the light extinction coefficient in the water column, and relatively insensitive to variations in incident solar radiation.     

Analysis of Water Management Scenarios Using Coupled Hydrological and System Dynamics Modeling

Water Resources Management - The North China Plain (NCP) has been affected by severe water scarcity over the past several decades. To address this issue, several water management plans have already...     

Rainfall-runoff Modeling in a Watershed Scale Using an Object Oriented Approach Based on the Concepts of System Dynamics

Relying on a linear causal thinking, most of the hydrological models fail to incorporate socio-economic characteristics of a watershed with hydrological and environmental attributes. Based on a systems thinking philosophy, the paper aims to adopt an Object-Oriented (OO) approach based on the concepts of System Dynamics (SD) such as stocks and flows to analyze the dynamics in a hydrological system in a watershed scale. Object-Oriented modeling is a way to organize data into discrete, recognizable entities called objects. These objects could be concrete (such as a river reach) or conceptual (such as a policy decision). In the present paper, VENSIM PLE has been used for the modeling purpose. The application was illustrated in an Iranian watershed. The model was examined using validity and verifying tests. The results showed that the model is capable of generating the monthly runoff quite well. The values of R² are 0.69 and 0.61 for generated discharge values at Polchehr and Doab stations respectively. Also the values of NSE are 0.66 and 0.64 for estimated discharge values at the same stations. The capability of model was more clarified comparing R² and NSE coefficients obtained by a SWAT model with those obtained by Watershed Hydrological Model developed in this study.     

Determining The Main Controlling Factors of Nitrogen Diffusion Fluxes at Sediment-water Interface by Grey Correlation Analysis

Water Resources Management - The hydro-fluctuation belt is a vital ecosystem with rich resources and environmental regulation ability. It plays a critical role in maintaining biodiversity, the...     

Evaluation of dike-type causeway impacts on the flow and salinity regimes in Urmia Lake,Iran

Urmia Lake, located in a closed basin in north-west Iran, is the largest lake (5000–6000 km²) in the Middle East. It is very saline with total dissolved salts reaching 200 g/l compared with a normal seawater salinity of about 35 g/l. The construction of a causeway, which was initiated in 1979 but then abandoned until the early 2000s, is near completion and will provide road access between the western and eastern provinces. The causeway has an opening 1.25 km long and divides Urmia Lake into a northern and southern basin and restricts water exchange. The flow and salinity regimes are affected by the presence of this new causeway, and there are concerns over the well being of the Artemia population. This study investigates the effects of the construction of the causeway on flow and salinity regimes, considers remedial actions, and examines the effects of climatic variability on salinity and flow. Flow and salinity regimes were numerically simulated by using a commercially available two and three-dimensional (2D and 3D) MIKE model. The validity of the numerical model was assessed through sensitivity analysis of the model and comparing the simulated results against field measurements; the 3D model provided the higher correlation between simulated and actual data. Wind input was the main climatic and hydrologic factor influencing flow regime while river discharge, evaporation and rainfall were the key parameters affecting salinity distribution in the lake models. The 3D model was subsequently used to predict lake conditions in typical dry, wet and normal climates, to examine the environmental impacts from the new causeway, and to evaluate possible improvements that some remedial measures may provide.     

近50年洞庭湖水位总体变化特征及成因分析

洞庭湖是长江中下游重要的吞吐型湖泊,由于地理位置和来水条件的特殊性,湖泊水位变动不仅受气候因素等自然条件的影响,同时受到长江和四水来水等的重要影响,特别是近50年来水利工程等人类活动影响加剧,洞庭湖水位变化呈现出新的变化特征和趋势。对近50年(1961年-2008年)洞庭湖水位的整体变化特征及年际和年内变化成因进行了分析。针对年际变化,重点分析了三峡等水利工程的影响,而年内变化,则主要对水位与各补给水源相关性进行了定量研究,得到丰枯水季各月水位变化的主要影响因素。     

海南小海洪潮特性及洪水位主要影响因素研究

在分析小海地区洪潮特性的基础上,应用二维水动力数学模型,研究小海洪水的产生及其影响因素,以及口门整治对小海洪水位的影响。研究表明,小海洪水位不仅受洪潮特性的影响,口门通道规模与洪水位的关系也很密切。     

Estimation of the Change in Lake Water Level by Artificial Intelligence Methods

In this study, five different artificial intelligence methods, including Artificial Neural Networks based on Particle Swarm Optimization (PSO-ANN), Support Vector Regression (SVR), Multi- Layer Artificial Neural Networks (MLP), Radial Basis Neural Networks (RBNN) and Adaptive Network Based Fuzzy Inference System (ANFIS), were used to estimate monthly water level change in Lake Beysehir. By using different input combinations consisting of monthly Inflow - Lost flow (I), Precipitation (P), Evaporation (E) and Outflow (O), efforts were made to estimate the change in water level (L). Performance of models established was evaluated using root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R²). According to the results of models, ε-SVR model was obtained as the most successful model to estimate monthly water level of Lake Beysehir.     

珠江三角洲洪水位变化主要影响因素研究

近年来,珠江三角洲河网区频频发生洪水,通过系统研究珠江三角洲洪水位变化的影响因素,综合分析各因素影响过程,最终得出珠江三角洲洪水位变化的主要影响因素.研究结果对珠江三角洲河道管理及洪水管理具有一定参考意义.     

Modeling of Water Main Failure Rates Using the Log-linear ROCOF and the Power Law Process

This paper presents applications of the log-linear ROCOF and the power law process to model the failure rate and estimate the economically optimal replacement time of the individual pipes in a water distribution system. The performance of the two failure rate models is examined using the maximized log-likelihoods for different modeling approaches in which the method of observing failures differs. The optimal replacement time equations for the two models are developed by applying the methodology of Loganathan et al. (J Water Resour Plan Manage ASCE 128(4):271–279, 2002) for the case in which modified time scales are used. It was found that the log-linear ROCOF showed better performance than the power law process when the ‘failure-time-based’ method is used. Furthermore, the ‘failure-time-based’ method is proved to be superior compared to the ‘failure-number-based’ method for the water mains under study, implying that recording each failure time results in better modeling of the failure rate than observing failure numbers in some time intervals.     

Advancing Freshwater Lake Level Forecast Using King’s Castle Optimization with Training Sample Adaption and Adaptive Neuro-Fuzzy Inference System

Water Resources Management - This study presents a novel method for more accurate forecasting freshwater Lake Levels with complex fluctuation patterns due to multiple anthropogenic demands and...     

Growth Dynamics of Cladophora Glomerata in Western Lake Erie in Relation to Some Environmental Factors

Cladophora was monitored at two sites in western Lake Erie during 1979 and 1980 as part of a lake-wide Lake Erie Cladophora Surveillance Program. Two distinctive zones within the littoral region were colonized by the alga, the eulittoral (splash zone) and infralittoral (defined in the present study as the 0.5–4 m depth zone). Cladophora of the eulittoral zone became established in May and remained present until late fall. The infralittoral zone Cladophora exhibited a bimodal growth pattern related to the seasonal temperature regime, with growth occurring from April to July and again from September to November. The infralittoral zone supported the largest share of biomass, which resulted in nuisance accumulations upon the beaches in the island region. Peak biomass was observed from mid-June to early July, obtaining maximum values of 102 gDW/m² and 214 gDW/m² for the 1979 and 1980 seasons, respectively. The depth to which Cladophora colonized was limited by light availability; maximum depth of growth occurred between 2 and 4 m in western Lake Erie due to the turbid nature of the basin. Phosphorus and nitrogen were not limiting to Cladophora growth in western Lake Erie; tissue nutrients remained above the critical levels defined by Gerloffand Fitzgerald (1976) throughout the season.     

Sustainability Analysis for Yellow River Water Resources Using the System Dynamics Approach

The water resource issue is one of the most significant problemsthat the Yellow River basin will face this century, and one which has received much attention by public and government for several years. Water authorities will face great challenges in meeting the in-stream flow requirements and providing more water for growing populations, industry and agriculture. In order toevaluate the sustainability of the water resource system inthe study area, an object-oriented system dynamics approachhas been used to develop a model for the water resourcessystem in the Yellow River basin, which is referred to asthe Water Resources System Dynamics (WRSD) model. It hasbeen developed for simulating a water resource system andcapturing the dynamic character of the main elements affectingwater demand and supply in the study area. For thebusiness-as-usual (BaU) scenario, the water demands in theYellow River basin are estimated 50.9, 56.5, and 59.5billion m³ for 2010, 2020, and 2030. The existing andpotential water supplies from surface water, aquifers andtreated waste-water are estimated, and potential waterdemands for domestic, industrial and agricultural uses areprojected. Various water supply and demand scenarios havethen been explored by changing variables and parameters,and the sustainability index of the water supply system isestimated for different sub-regions over various periods.     

Dynamics of PCBs in the Food Web of Lake Winnipeg

The bioaccumulation of polychlorinated biphenyls (PCBs) is examined in the food web of Lake Winnipeg using measured contaminant concentrations, stable isotopes of nitrogen, and a food web model. Measured concentrations of the sum of 103 PCB congeners are higher in south basin water, sediment, and biota compared with the north. The trophic positions of the top predators as well as the extent of biomagnification of PCBs per unit trophic level do not differ significantly between the north and the south basins. We therefore conclude that the higher PCB concentrations in the south basin are due primarily to higher PCB loadings via riverine sources to the south rather than food web processes. In contrast, the data from the north basin suggest lower total loadings of which a higher fraction is from atmospheric deposition. We find that rainbow smelt (Osmerus mordax) are not associated with elevated exposure of contaminants to top predators of the north basin. This surprising result is attributed to their reduced fitness in this relatively shallow and warm system, which may prevent them from feeding at an elevated trophic level compared with other forage fish. Finally, we hypothesize that high nutrient associated DOC in water decreases PCB bioavailability to lower trophic level organisms and hence the entire food web.     

调节鄱阳湖枯水位 维护江湖健康

2003年以来,鄱阳湖枯水期水位连创新低,持续时间延长,导致水质恶化、湿地生态系统遭受破坏、候鸟栖息地受到威胁;生产生活用水、农田灌溉、航运和水产捕捞受到严重影响．为了改变鄱阳湖枯水期持续处于低水位状态,确保江湖水系健康,提出了鄱阳湖枯水调节的基本原则：改善生态环境为主要目的,调枯不调洪;从整体出发,做到江湖两利;以简单工程进行调节,趋利避害;保持工程运用的灵活性,实施适应性管理．同时,规划了鄱阳湖枯水调节工程的地点、规模和运行方式．作为一个以保护和改善生态环境为主的水利工程,其具有改善水环境、保护水生态、维护水安全,保护鄱阳湖湿地的生态环境及确保候鸟越冬条件,缓解长江下游枯水期低水位状态和促进湖区经济社会发展等方面的效益．