Comparison of Recurrent Neural Network,Adaptive Neuro-Fuzzy Inference System and Stochastic Models in Eğirdir Lake Level Forecasting

Accurate prediction of lake-level changes is a very important problem for a wise and sustainable use. In recent years significant lake level fluctuations have occurred and can be related to the climatic change. Such a problem is crucial to the works and decisions related to the water resources and management. This study is aimed to predict future lake levels during hydrometeorological changes and anthropogenic activities taking place in the Lake Eğirdir which is the most important water storage of Lake Region, one of the biggest fresh water lakes of Turkey. For this aim, recurrent neural network (RNN), adaptive network-based fuzzy inference system (ANFIS) as prediction models which have various input structures were constructed and the best fit model was investigated. Also, the classical stochastic models, auto-regressive (AR) and auto-regressive moving average (ARMA) models are generated and compared with RNN and ANFIS models. The performances of the models are examined with the form of numerical and graphical comparisons in addition to some statistic efficiency criteria. The results indicated that the RNN and ANFIS can be applied successfully and provide high accuracy and reliability for lake-level changes than the AR and the ARMA models. Also it was shown that these stochastic models can be used in the lake management policies with the acceptable risk.     

Comparing ANN and ARIMA model in predicting the discharge of River Opeki from 2010 to 2020

Many attempts have been made in the recent past to model and forecast streamflow using various techniques with the use of time series techniques proving to be the most common. Time series analysis plays an important role in hydrological research. Traditionally, the class of autoregressive moving average techniques models has been the statistical method most widely used for modelling water discharge, but it has been shown to be deficient in representing nonlinear dynamics inherent in the transformation of runoff data. In contrast, the relatively newly improved and efficient soft computing technique artificial neural networks has the capability to approximate virtually any continuous function up to an arbitrary degree of accuracy, which is not otherwise true of other conventional hydrological techniques. This technique corresponds to human neurological system, which consists of a series of basic computing elements called neurons, which are interconnected together to form networks. The aim of the study is to compare the artificial neural network and autoregressive integrated moving average to model River Opeki discharge (1982–2010) and to use the best predictor to forecast the discharge of the river from 2010 to 2020. The performance of the two models was subjected to statistical test based on correlation coefficient (r) and the root‐mean‐square error. The result showed that autoregressive integrated moving average performs better considering the level of root‐mean‐square error and higher correlation coefficient.     

Short-Term Water Demand Forecast Modelling at IIT Kanpur Using Artificial Neural Networks

The efficient operation and management of an existing water supply system require short-term water demand forecasts as inputs. Conventionally, regression and time series analysis have been employed in modelling short-term water demand forecasts. The relatively new technique of artificial neural networks has been proposed as an efficient tool for modelling and forecasting in recent years. The primary objective of this study is to investigate the relatively new technique of artificial neural networks for use in forecasting short-term water demand at the Indian Institute of Technology, Kanpur. Other techniques investigated in this study include regression and time series analysis for comparison purposes. The secondary objective of this study is to investigate the validity of the following two hypotheses: 1) the short-term water demand process at the Indian Institute of Technology, Kanpur campus is a dynamic process mainly driven by the maximum air temperature and interrupted by rainfall occurrences, and 2) occurrence of rainfall is a more significant variable than the rainfall amount itself in modelling the short-term water demand forecasts. The data employed in this study consist of weekly water demand at the Indian Institute of Technology, Kanpur campus, and total weekly rainfall and weekly average maximum air temperature from the City of Kanpur, India. Six different artificial neural network models, five regression models, and two time series models have been developed and compared. The artificial neural network models consistently outperformed the regression and time series models developed in this study. An average absolute error in forecasting of 2.41% was achieved from the best artificial neural network model, which also showed the best correlation between the modelled and targeted water demands. It has been found that the water demand at the Indian Institute of Technology, Kanpur campus is better correlated with the rainfall occurrence rather than the amount of rainfall itself.     

神经网络模拟降雨径流过程

本文根据水文现象的特性建议了两个网络模型——实时输出反馈网络(ROBIN)和奢侈输出反馈网络(ADONIS)，并与水文模拟网络(HYMN)和传统水箱(TANK)模型进行了比较.结果表明建议的两个网络模型是可行的.     

气候变化对可可西里盐湖流域湖泊水量变化的影响分析

湖泊是气候变化的敏感指示器。为了研究气候变化对湖泊水量的影响,以盐湖流域为研究区,应用统计方法对1989—2018年降雨、气温、蒸发进行线性趋势和突变分析,采用多源卫星遥感技术对湖泊面积等水文要素进行监测,分析湖泊面积与气象要素、湖泊面积与湖泊水量之间的相关性。利用VIC模型模拟径流并结合计算的冰川水量得到盐湖径流组成,定量探讨气象要素对湖泊水量变化的影响,综合分析2011年前后气象要素影响流域湖泊水量的差异。结合统计分析与水文模型定量计算可知:年降雨量、年平均气温显著升高,年蒸发量呈下降趋势,且与湖泊面积有较好的相关性。湖泊面积与湖泊水量间相关性较高,可间接体现气象要素对湖泊水量变化的影响。2011年前卓乃湖和盐湖水量变化主要受降雨量影响,库赛湖和海丁诺尔湖水量变化主要受气温影响;2011—2014年4个湖泊水量变化主要受降雨量影响;2015—2018年4个湖泊水量变化中降雨增加量、冻土释水和地下水补给增加量、冰川融水量对湖泊扩张的贡献约为34.48%、57.66%、7.86%,气温变化成为影响湖泊水量变化的主要因素,降雨量影响次之。     

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 .

     

七浦塘引水对阳澄湖河网水环境影响的模拟研究

为准确地衡量七浦塘引水工程的运行方式对阳澄湖及周边河网产生的影响,基于太湖水量水质模型设计了7种数值实验,从引水量和雨型两个角度对七浦塘引水对阳澄湖及周边河网造成的影响进行了评估,并定义了河网总体水质达标率作为评估标准。结果表明:七浦塘引水使得水量向阳澄湖北线和南线河道集中,在改善阳澄湖西湖水质的同时恶化了中东湖的水质;七浦闸引水量在20~40 m3/s之间时对提高河网水质总体达标率最为有利;引水对河网水质的影响具有显著的空间差异性;在不同雨情条件下,七浦塘引水调度均可在一定程度上改善河网和湖体的水质。     

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.     

The responses of Nile tilapia Oreochromis niloticus (Linnaeus, 1758) in Lake Wamala (Uganda) to changing climatic conditions

Changes in the catches of Nile tilapia, Oreochromis niloticus (Linnaeus, 1758), in Lake Wamala (Uganda) have been observed since its introduction. The factors contributing to these changes, however, are not well understood. This study examined changes in species composition, size structure, size at first maturity, length–weight relationship and condition factor of Nile tilapia in Lake Wamala, in relation to changes in temperature, rainfall and lake depth, to provide a better understanding of the possible role of changing climatic conditions. There was an increase in the minimum, maximum and average temperatures since 1980, but only the minimum (0.021 °C year^?1) and average temperatures (0.018 °C year^?1) exhibited a significant trend (P < 0.05). Rainfall increased by 8.25 mm year^?1 since 1950 and accounted for 79.5% of the water input into the lake during the period 2011–2013, while evaporation accounted for 86.2% of the water loss from the lake. The lake depth was above 4 m during the years when the rainfall exceeded the average of 1180 mm, except after 2000. The contribution of Nile tilapia to total fish catch and catch per unit effort (CPUE) increased with rainfall and lake depth up to the year 2000, after which they decreased, despite an increased rainfall level. The lake depth was positively correlated with the average total length and length at 50% maturity (r = 0.991 and 0.726, respectively), while the slopes of the length–weight relationships differed significantly between high and low lake depths [t₍₆₎ = 3.225, P < 0.05]. Nile tilapia shifted from an algal‐dominated diet during the wet season to include more insects during the dry season. The results of this study indicate Nile tilapia in Lake Wamala displays a typical r‐selected reproductive strategy, by growing to a small size, maturing faster and feeding on different food types, in order to survive high mortality rates under unfavourable conditions attributable to higher temperatures, low rainfall and low lake water levels.     

基于Landsat卫星数据的洪湖水体遥感监测研究

2011年洪湖遭遇了70 a一遇的干旱,湖区水体面积发生了明显的变化。为了解影响洪湖水体面积变化的因素,利用1973年以来的多期Landsat卫星遥感图像数据,提取洪湖湖区水域面积,结合同期逐月日平均降雨量数据,对洪湖水体面积变化进行了探讨。分析表明：洪湖水体面积呈下降趋势;1973~2000年洪湖水体面积和年降雨量数据、汛期降雨量数据的相关性都很高,水体面积的变化趋势和年降雨量的变化趋势基本一致;而1973~2010年洪湖水体面积和年降雨量数据、汛期降雨量数据的相关性都有所降低,说明2000年后,影响洪湖水体面积的因素可能存在降雨量之外的其他因素,具体情况还有待进一步深入研究。