Incorporating Future Climatic and Socioeconomic Variables in Water Demand Forecasting: A Case Study in Bangkok

With concerns relating to climate change, and its impacts on water supply, there is an increasing emphasis on water utilities to prepare for the anticipated changes so as to ensure sustainability in supply. Forecasting the water demand, which is done through a variety of techniques using diverse explanatory variables, is the primary requirement for any planning and management measure. However, hitherto, the use of future climatic variables in forecasting the water demand has largely been unexplored. To plug this knowledge gap, this study endeavored to forecast the water demand for the Metropolitan Waterworks Authority (MWA) in Thailand using future climatic and socioeconomic data. Accordingly, downscaled climate data from HadCM3 and extrapolated data of socioeconomic variables was used in the model development, using Artificial Neural Networks (ANN). The water demand was forecasted at two scales: annual and monthly, up to the year 2030, with good prediction accuracy (AAREs: 4.76 and 4.82 % respectively). Sensitivity analysis of the explanatory variables revealed that climatic variables have very little effect on the annual water demand. However, the monthly demand is significantly affected by climatic variables, and subsequently climate change, confirming the notion that climate change is a major constraint in ensuring water security for the future. Because the monthly water demand is used in designing storage components of the supply system, and planning inter-basin transfers if required, the results of this study provide the MWA with a useful reference for designing the water supply plan for the years ahead.     

长江流域水资源预测技术

水资源预测作为现有水文、气象预报的一种拓展,必须以现有水文、气象预报手段为基础,充分利用现有的信息化技术,从气候、地理、水文水资源、水环境保护等不同领域对影响水资源变化的诸多因素进行综合分析,构建水资源预测信息平台,加强对气候、水资源等方面的基础性分析研究,开展短期气候预测技术应用研究.开展长江流域水资源预测还需要结合流域内经济发展指标,对长江流域可供水资源、工农业用水、居民生活用水、生态环境用水需求量、水质监测等信息进行收集整理和分析,通过对长江流域或流域内某一地区未来降雨量的预测,对该地区可能形成的地表径流量、水质等作进一步预测,并分析其与用水需求量之间的基本规律和经验关系,建立适合长江流域水资源预测模型,并对流域内重点区域水资源质、量在未来一定时间尺度内的变化趋势进行预测,建立相应的水资源供需关系的预警机制.目的是更好地合理利用水资源、优化水资源配置,为长江流域经济和社会的可持续发展提供强有力的技术支撑.     

Seq2Seq 模型的短期水位预测

为有效预测未来一定时间内的连续水位,提出了基于序列到序列(Seq2Seq) 的短期水位预测模型,并使用一个长短期记忆神经网络(LSTM)作为编码层,将历史水位序列编码为一个上下文向量,使用另一个LSTM 作为解码层,将上下文向量解码来预测目标水位序列。以流溪河为研究对象,针对不同预测长度分别建立水位预测模型,并与LSTM 模型和人工神经网络(ANN)模型进行了对比。结果表明:Seq2Seq 模型对连续6 h、12 h 和24 h 水位预测的纳什效率系数最高分别为0.93、0.90和0.85;当预测长度为6 h 时,LSTM 和Seq2Seq 模型预测结果相似,ANN 模型精度较低;当预测长度为12 h 和24 h 时,Seq2Seq 模型相比LSTM 模型和ANN 模型预测效果更好,收敛速度更快。     

Short-Term Urban Water Demand Prediction Considering Weather Factors

Accurate and reliable forecasting plays a key role in the planning and designing of municipal water supply infrastructures. Recent studies related to water demand prediction have shown that water demand is driven by weather variables, but the results do not clearly show to what extent. The principal aim of this research was to better understand the effects of weather variables on water demand. Additionally, it aimed to offer an appropriate and reliable technique to predict municipal water demand by using the Gravitational Search Algorithm (GSA) and Backtracking Search Algorithm (BSA) with Artificial Neural Network (ANN). Moreover, eight weather factors were adopted to evaluate their impact on the water demand. The principal findings of this research are that the hybrid GSA-ANN (Agent?=?40) model is superior in terms of fitness function (based on RMSE) for yearly and seasonal phases. In addition, it is evidently clear from the findings that the GSA-ANN model has the ability to simulate both seasonal and yearly patterns for daily data water consumption.     

基于随机森林模型的需水预测模型及其应用

为解决需水预测模型精度问题,尝试基于随机森林模型的分类和回归功能构建需水预测模型。以苏州市需水量预测为研究实例,首先应用随机森林模型的分类功能将需水预测因子分类,经计算发现第一产业比例、人口、灌溉面积、万元产值用水量和国民经济生产总值为最重要的解释变量。在此基础上,用随机森林模型的回归功能对需水进行预测,同时采用相同的训练数据建立基于BP神经网络和RBF神经网络的需水预测模型,通过对比3个模型的预测结果,发现随机森林模型能有效预测需水量,且精度较高。     

基于主要驱动因子筛选法和深度学习算法的浙江省动态需水量预测

收集了浙江省2000—2020年各用水行业需水量数据,采用基于Spearman秩相关分析的主要驱动因子筛选法筛选了影响各行业需水量的主要驱动因子,进而构造了改进的长短时记忆(LSTM)神经网络需水量预测模型,对各行业需水量进行动态滚动预测,并将改进LSTM模型的预测结果与传统单变量LSTM预测模型、卷积神经网络模型、支持向量回归模型的预测结果进行了对比。结果表明,基于主要驱动因子筛选法改进的LSTM模型能实时动态滚动预测各行业每年需水量,且预测结果精度高于其他3种模型。     

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.     

东海县需水量预测研究

为了提高对东海县需水量的预测精度,将比重较大且表现出很高随机性的灌溉需水量从需水总量中单独划出,采用定额法按不同频率年型对其进行预测;以灰色预测模型和三次指数平滑模型的组合预测法对非灌溉需水量进行预测,在确定单个模型的权重时更多地考虑了近期残差对预测值的影响;将灌溉需水量和非灌溉需水量汇总即可得到最终的预测值.该方法对数据要求不高并且能够按照不同的概率给出相应的需水量范围,从东海县需水量的历史数据可以判断最终的预测成果比较合理.     

基于BP神经网络的海河干流叶绿素浓度短时预测研究

为实时预测海河干流水体藻华的暴发时段及影响程度,提高环境管理部门决策能力,以海河干流段典型断面的水质在线监测及气象站高频、实时数据为基础,基于BP神经网络,以实时叶绿素浓度、气温、光照强度和气压四项指标为输入变量,建立了叶绿素浓度日变化量的预测模型,对海河干流大光明桥处水域叶绿素浓度随时间的变化进行预测。结果表明:对海河干流叶绿素浓度短时预测影响较大的因素依次为溶解氧(叶绿素)、气温、光照强度、气压、降雨、电导率、相对湿度;预测时长越短,预测精度越高。当预测时长分别为24 h、12 h、6 h时,Nash效率系数分别为0.77、0.85、0.93,预报误差的标准误差分别为5.7μg/L、4.6μg/L、3.1μg/L;12 h内的预测精度可满足海河河道藻华预警的实际需求,为其短期预警提供了数据支撑。     

Water Demand Forecasting Model for the Metropolitan Area of São Paulo,Brazil

This work is concerned with forecasting water demand in the metropolitan area of São Paulo (MASP) through water consumption, meteorological and socio-environmental variables using an Artificial Neural Network (ANN) system. Possible socio-environmental and meteorological conditions affecting water consumption at Cantareira water treatment station (WTS) in the MASP, Brazil were analyzed for the year 2005. Eight model configurations were developed and used for the Cantareira WTS. The best performance was obtained for 12-h average of the input variables. The ANN model performed best with three times steps in advance. The hourly forecasting was obtained with acceptable error levels. Model results indicate an overall tendency for small errors. The proposed method is useful tool for water demand forecasting and water systems management. The paper is an important contribution since it takes into account weather variables and introduces some diagnostic studies on water consumption in one of the largest urban environments of the planet with its unique peculiarities such as anthropic affects on weather and climate that feeds back into the water consumption. The averaging is a low pass filter indeed and we used it to improve Signal to Noise Ratio (SNR).     

Monthly Rainfall Prediction Using Wavelet Neural Network Analysis

Rainfall is one of the most significant parameters in a hydrological model. Several models have been developed to analyze and predict the rainfall forecast. In recent years, wavelet techniques have been widely applied to various water resources research because of their time-frequency representation. In this paper an attempt has been made to find an alternative method for rainfall prediction by combining the wavelet technique with Artificial Neural Network (ANN). The wavelet and ANN models have been applied to monthly rainfall data of Darjeeling rain gauge station. The calibration and validation performance of the models is evaluated with appropriate statistical methods. The results of monthly rainfall series modeling indicate that the performances of wavelet neural network models are more effective than the ANN models.     

Reference Evapotranspiration Prediction Using Neural Networks and Optimum Time Lags

The reference evapotranspiration (ET₀) plays a significant role especially in agricultural water management and water resources planning for irrigation. It can be calculated using different empirical equations and forecasted by applying various artificial intelligence techniques. The simulation result of a machine learning technique is a function of its structure and model inputs. The purpose of this study is to investigate the effect of using the optimum set of time lags for model inputs on the prediction accuracy of monthly ET₀ using an artificial neural network (ANN). For this, the weather data time-series i.e. minimum and maximum air temperatures, vapour pressure, sunshine hours, and wind speed were collected from six meteorological stations in Serbia for the period 1980–2010. Three ANN models were applied to monthly ET₀ time-series to study the impacts of using the optimum time lags for input time-series on the performance of ANN model. Achieved results of goodness–of–fit statistics approved the results obtained by scatterplots of testing sets - using more time lags that are selected based on their correlation to the dataset is more efficient for monthly ET₀ prediction. It was realized that all the developed models showed the best performances at Loznica and Vranje stations and the worst performances at Nis station. Simultaneous assessment of the impact of using a different number of time lags and the set of time lags that show a stronger correlation to the dataset for input time-series, on the performance of ANN model in monthly ET₀ prediction in Serbia is the novelty of this study.

     

Assessing Socioeconomic Drought Based on a Standardized Supply and Demand Water Index

Socioeconomic drought occurs when a water shortage is caused by an imbalance between the supply and demand of water resources in natural and human socioeconomic systems. Compared with meteorological drought, hydrological drought, and agricultural drought, socioeconomic drought has received relatively little attention. Hence, this study aims to construct a universal and relatively simple socioeconomic drought assessment index, the Standardized Supply and Demand Water Index (SSDWI). Taking the Jianjiang River Basin (JJRB) in Guangdong Province, China, as an example, we analyzed the socioeconomic drought characteristics and trends from 1985 to 2019. The return periods of different levels of drought were calculated. The relationships among socioeconomic, meteorological, and hydrological droughts and their potential drivers were discussed. Results showed that: (1) SSDWI can assess the socioeconomic drought conditions well at the basin scale. Based on the SSWDI, during the 35-year study period, 29 socioeconomic droughts occurred in the basin, with an average duration of 6.16 months and average severity of 5.82. Socioeconomic droughts mainly occurred in autumn and winter, which also had more severe droughts than other seasons. (2) In the JJRB, the joint return periods of “∪” and “∩” for moderate drought, severe drought, and extreme drought were 8.81a and 10.81a, 16.49a and 26.44a, and 41.68a and 91.13a, respectively. (3) Because of the increasing outflow from Gaozhou Reservoir, the occurrence probability of socioeconomic drought and hydrological drought in the JJRB has declined significantly since 2008. Reservoir scheduling helps alleviate hydrological and socioeconomic drought in the basin.

     

A Methodology to Model Water Demand based on the Identification of Homogenous Client Segments. Application to the City of Barcelona

Water management has become a vital concern for both water supply companies and public administrations due to the importance of water for life and current scarcity in many areas. Studies exist that attempt to explain which factors influence water demand. In general, these studies are based on a small sample of consumers and they predict domestic water consumption using ordinary least squares regression models with a small number of socioeconomic variables as predictors, usually: price, population, population density, age, and nationality. We have followed a different approach in two ways; one, in the scope of the study: we have included in the study all consumers of the Barcelona area and as many socioeconomic variables as possible (all the available data from official statistics institutions); and also in the methodology: first, we have segmented clients into homogeneous socioeconomic groups that, as we show later in the Barcelona case, also have homogeneous water consumption habits. This allows for a better understanding of water consumption behaviours and also for better predictions through modeling water consumption in each segment. This is so because the segments’ inner variability is smaller than the general one; thus, the models have a smaller residual variance and allow for more accurate forecasts of water consumption. The methodology was applied to the Barcelona metropolitan area, where it was possible to construct a database including both water consumption and socioeconomic information with more than one million observations. Data quality was a primary concern, and thus a careful exploratory data analysis procedure led to a careful treatment of missing observations and to the detection and correction or removal of anomalies. This has resulted in a stable division of the one million water consumers into 6 homogeneous groups and models for each of the groups. Although the methodology has been developed and applied to the Barcelona area, it is general and thus can be applied to any other region or metropolitan area.     

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.     

Artificial Neural Network and Support Vector Machine Models for Inflow Prediction of Dam Reservoir (Case Study: Zayandehroud Dam Reservoir)

Inflow prediction of reservoirs is of considerable importance due to its application in water resources management related to downstream water release planning and flood protection. Therefore, in this research, different new input patterns for predicting inflow to Zayandehroud dam reservoir is proposed employing artificial neural network (ANN) and support vector machine (SVM) models. Nine different models with different patterns of input data such as inflow to the dam reservoir considering time duration lags, time index, and monthly rainfall of Ghaleh-Shahrokh station have been proposed to predict the inflow to the dam reservoir. Comparison of the results indicates that the ninth proposed model has the least error for inflow prediction in which the results of SVM model outperform those of ANN model. That is, the least error has been obtained using the ninth SVM (ANN) model with correlation coefficient (R) values of 0.8962 (0.89296), 0.9303 (0.92983) and 0.9622 (0.95333) and root mean squared error (RMSE) values of 47.9346 (48.5441), 42.69093 (43.748) and 23.56193 (28.5125) for training, validation and test data, respectively.

     

Neural networks modelling of streamflow, phosphorus, and suspended solids: application to the Canadian Boreal forest.

Sediment has long been identified as an important vector for the transport of nutrients and contaminants such as heavy metals and microorganisms. The respective nutrient loading to water bodies can potentially lead to dissolved oxygen depletion, cyanobacteria toxin production and ultimately eutrophication. This study proposed an artificial neural network (ANN) modelling algorithm that relies on low cost readily available meteorological data for simulating streamflow (Q), total suspended solids (TSS) concentration, and total phosphorus (TP) concentration. The models were applied to a 130-km2 watershed in the Canadian Boreal Plain. Our results demonstrated that through careful manipulation of time series analysis and rigorous optimization of ANN configuration, it is possible to simulate Q, TSS, and TP reasonably well. R2 values exceeding 0.89 were obtained for all modelled data cases. The proposed models can provide real time predictions of the modelled parameters, can answer questions related to the impact of climate change scenarios on water quantity and quality, and can be implemented in water resources management through Monte Carlo simulations.     

微动力曝气技术应用于黑臭水体治理的参数优化研究

为了得到微动力曝气技术在黑臭水体治理过程中优化后的条件参数及预测模型,以曝气量、曝气时长和曝气位置为自变量,脱氮效果(氨氮消除时长和总氮削减率)为响应变量,根据Design-expert设置了17组试验。在试验过程中考虑内源污染释放等因素,然后以试验数据为基础,结合响应面模型分析,研究得出优化后的条件参数和预测模型。研究结果表明:两个响应面模型调整后的拟合度分别为0.99和0.96,预测模型的拟合度为0.95和0.69,具有良好的拟合度;优化后的条件参数曝气量为1 L/min、曝气时长12 h/d和曝气位置位于上覆水中部;此试验组下的氨氮消除时长和总氮削减率分别为5.50 d±0.00 d和51.84%±1.14%,与预测模型的预测值相比较,两者标准差分别为0.00%和0.98%。最终得出结论:优化条件参数后的微动力曝气技术应用于黑臭水体治理可以取得较好脱氮效果,模型可以较准确地预测水体修复效果。     

灰色关联—集对聚类预测模型在吉林省用水量预测中的应用

对地区未来用水量进行预测对于实现水资源的合理规划与调度有着重要意义。为了对吉林省未用水量进行合理预测,建立了吉林省短期用水量预测的灰关联-集对聚类预测模型,并用吉林省实际用水量数据对模型进行了交叉精度检验。结果发现:该模型对吉林省2015用水量预测结果与实际数据的相对误差为2.00%,预测精度好于灰色预测模型和BP神经网络模型。20年数据检验平均误差为2.675%,预测效果较好,可用于区域未来用水量预测。根据此模型以及吉林省发展规划,2020年吉林省用水量将达到138.74×10~8m~3。     

黑河流域水资源实时调度系统

本文结合黑河流域实际情况和流域发展的现实需求，提出了“宏观总控、长短嵌套、实时决策、滚动修正”的流域水资源实时调度的模式，并应用自适应控制方法，建立了流域水资源的实时调度系统，包括相互嵌套的实时调度预报子系统、实时调度决策子系统和实时修正子系统。实时调度预报子系统主要包括长期和短期径流预报、土壤墒情预报和需水预报；实时调度决策子系统包括长期和短期调度决策；实时修正子系统是根据采集的实时信息，进行长期和短期调度的修正。