Water resources planning under climate and economic changes in Skiathos island, Aegean

Small Aegean islands are facing complicated pressures of different natures. Their physically limited water resources are invoked to cover the increasing needs of the local population, combined with the seasonal water demand peaks due to tourists. This often leads to aquifers’ overexploitation and seawater intrusion, deteriorating the water quality. Water scarcity may also occur due to inadequate infrastructure, limited investments and human resources for proper management. This study uses the example of Skiathos island, which faces all the above challenges. The water supply network and the city's demand are simulated through WEAP software, in an attempt to address the major drivers for future water management. A long-term water demand forecast is performed under scenarios of climate change (based on ensemble means of RCP simulations), and water pricing (based on the recommendations of European legislation). Other pressures (i.e., operation of new hotels) and measures (i.e., desalinization unit, network and reservoir works) that were already considered by the local authorities, are discussed. Overall, the findings aim to sensitize and motivate local policymakers to construct databases, start monitoring, include more factors in the decision-making process, and avoid overexploitation for the sake of non-sustainable development norms.

Application of deep learning methods for urban water demand forecast modelling

<p>Water demand predictions forms an integral part of sustainable management practices for water supply systems. Demand prediction models aides in water system maintenance, expansions, daily operational planning and in the development of an efficient decision support system based on predictive analytics. In recent years, it has also found wide application in real-time control and operation of water systems as well. However, short term water demand forecasting is a challenging problem owing to the frequent variations present in the urban water demand patterns. There are numerous methods available in literature that deals with water demand forecasting. These methods can be roughly classified into statistical and machine learning methods. The application of deep learning methods for forecasting water demands is an upcoming research area that has found immense traction due to its ability to provide accurate and scalable models. But there are only a few works which compare and review these methods when applied to a water demand dataset. Hence, the main objective of this work is the application of different commonly used deep learning methods for development of a short-term water demand forecast model for a real-world dataset. The algorithms studied in this work are (i) Multi-Layer Perceptron (MLP) (ii) Gated Recurrent Unit (GRU) (iii) Long Short-Term Memory (LSTM) (iv) Convolutional Neural Networks (CNN) and (v) the hybrid algorithm CNN-LSTM. Optimal supervised learning framework required for forecasting the one day ahead water demand for the study area is also identified. The dataset used in this study is from Hillsborough County, Florida, US. The water demand data was available for a duration of 10 months and the data frequency is about once per hour. These algorithms were evaluated based on the (1) Mean Absolute Percentage Error (MAPE) and (ii) Root Mean Squared Error (RMSE) values. Visual comparison of the predicted and true demand plots was also employed to check the prediction accuracy. It was observed that, the RMSE and MAPE values were minimal for the supervised learning framework that used the previous 24-hour data as input. Also, with respect to the forecast accuracy, CNN-LSTM performed better than the other methods for demand forecast, followed by MLP. MAPE values for the developed deep learning models ranged from 5% to 25%. The quantity, frequency and quality of data was also found to have substantial impact on the accuracy of the forecast models developed. In the CNN-LSTM based forecast model, the CNN component was found to effectively extract the inherent characteristics of historical water consumption data such as the trend and seasonality, while the LSTM part was able to reflect on the long-term historical process and future trend. Thus, its water demand prediction accuracy was improved compared to the other methods such as GRU, MLP, CNN and LSTM.</p>

Water demand forecast model of Least Squares Support Vector Machine based on Particle Swarm Optimization

In order to solve the problem of precision of water demand forecast model, a coupled water demand forecast model of particle swarm optimization (PSO) algorithm and least squares support vector machine (LS-SVM) are proposed in this paper. A PSO-LSSVM model based on parameter optimization was constructed in a coastal area of Binhai, Jiangsu Province, and the total water demand in 2009 and 2010 were simulated and forecasted with the absolute value of the relative errors less than 2.1%. The results showed that the model had good simulation effect and strong generalization performance, and can be widely used to solve the problem of small- sample, nonlinear and high dimensional water demand forecast.

Water Demand Forecast in the Baiyangdian Basin with the Extensive and Low-Carbon Economic Modes

The extensive and low-carbon economic modes were constructed on the basis of population, urbanization level, economic growth rate, industrial structure, industrial scale, and ecoenvironmental water requirement. The objective of this paper is to quantitatively analyze effects of these two economic modes on regional water demand. Productive and domestic water demands were both derived by their scale and quota. Ecological water calculation involves the water within stream, wetland, and cities and towns. Total water demand of the research region was obtained based on the above three aspects. The research method was applied in the Baiyangdian basin. Results showed that total water demand with the extensive economic mode would increase by 1.27 billion m3, 1.53 billion m3, and 2.16 billion m3in 2015, 2020, and 2030, respectively, compared with that with low-carbon mode.

Water Supply and Consumption Structure Change and Water Demand Forecast in Northern Shaanxi

This paper analyzed the water resources and its availability distribution regularities in Northern Shaanxi; and the change laws of water consumption and supply in 1980-2010; according to the relevant planning goal and various industry water standard, forecasted the Northern Shaanxi water demand in future. Result shows that 2020 and 2030 water demand respectively is 1.9×109 m3 and 2.6×109 m3 in Northern Shaanxi. So the 1.6×109 m3 of available water resources at this stage can't meet the future requirements.