ARIMA-M: A New Model for Daily Water Consumption Prediction Based on the Autoregressive Integrated Moving Average Model and the Markov Chain Error Correction

Spatio-temporal evolution mechanism and dynamic simulation of nitrogen and phosphorus pollution of the Yangtze River economic Belt in China

Excess nitrogen and phosphorus inputs are the main causes of aquatic environmental deterioration. Accurately quantifying and dynamically assessing the regional nitrogen and phosphorus pollution emission (NPPE) loads and influencing factors is crucial for local authorities to implement and formulate refined pollution reduction management strategies. In this study, we constructed a methodological framework for evaluating the spatio-temporal evolution mechanism and dynamic simulation of NPPE. We investigated the spatio-temporal evolution mechanism and influencing factors of NPPE in the Yangtze River Economic Belt (YREB) of China through the pollution load accounting model, spatial correlation analysis model, geographical detector model, back propagation neural network model, and trend analysis model. The results show that the NPPE inputs in the YREB exhibit a general trend of first rising and then falling, with uneven development among various cities in each province. Nonpoint sources are the largest source of land-based NPPE. Overall, positive spatial clustering of NPPE is observed in the cities of the YREB, and there is a certain enhancement in clustering. The GDP of the primary industry and cultivated area are important human activity factors affecting the spatial distribution of NPPE, with economic factors exerting the greatest influence on the NPPE. In the future, the change in NPPE in the YREB at the provincial level is slight, while the nitrogen pollution emissions at the municipal level will develop towards a polarization trend. Most cities in the middle and lower reaches of the YREB in 2035 will exhibit medium to high emissions. This study provides a scientific basis for the control of regional NPPE, and it is necessary to strengthen cooperation and coordination among cities in the future, jointly improve the nitrogen and phosphorus pollution tracing and control management system, and achieve regional sustainable development.

Water demand in watershed forecasting using a hybrid model based on autoregressive moving average and deep neural networks

Increasing water demand is exacerbating water shortages in water-scarce regions (such as India, China, and Iran). Effective water demand forecasting is essential for the sustainable management of water supply systems in watersheds. To alleviate the contradiction between water supply and demand in the basin, with water demand for economic growth as the main target, a hybrid moving autoregressive and deep neural network model (ARMA-DNN) was developed in this study, and four commonly used statistical indicators (MAE, RMSE, MSE, and R²) were selected to evaluate the performance of the model. Finally, the validity and practicality of the model were verified by taking the Minjiang River basin in China as an example. The results show that (a) the model can predict future water demand more accurately under the conditions of actual water consumption changes, (b) the ideal agricultural production in the Minjiang River Basin is predicted to be reached 2.26 × 10⁹t in 2021, and (c) the highest industrial economic efficiency in Chengdu is 1.51 × 10⁹yuan, while water satisfaction reaches 102%. This means that effective water demand forecasting can alleviate water demand conflicts under climate change conditions to a certain extent. At the same time, watershed managers can develop different water allocation schemes based on the prediction results of the hybrid ARMA-DNN model.

Advanced Techniques for Monitoring and Management of Urban Water Infrastructures—An Overview

Water supply systems are essential for a modern society. This article presents an overview of the latest research related to information and communication technology systems for water resource monitoring, control and management. The main objective of our review is to show how emerging technologies offer support for smart administration of water infrastructures. The paper covers research results related to smart cities, smart water monitoring, big data, data analysis and decision support. Our evaluation reveals that there are many possible solutions generated through combinations of advanced methods. Emerging technologies open new possibilities for including new functionalities such as social involvement in water resource management. This review offers support for researchers in the area of water monitoring and management to identify useful models and technologies for designing better solutions.

Development of a Deep Learning-Based Prediction Model for Water Consumption at the Household Level

The importance of efficient water resource supply has been acknowledged, and it is essential to predict short-term water consumption in the future. Recently, it has become possible to obtain data on water consumption at the household level through smart water meters. The pattern of these data is nonlinear due to various factors related to human activities, such as holidays and weather. However, it is difficult to accurately predict household water consumption with a nonlinear pattern with the autoregressive integrated moving average (ARIMA) model, a traditional time series prediction model. Thus, this study used a deep learning-based long short-term memory (LSTM) approach to develop a water consumption prediction model for each customer. The proposed model considers several variables to learn nonlinear water consumption patterns. We developed an ARIMA model and an LSTM model in the training dataset for customers with four different water-use types (detached houses, apartment, restaurant, and elementary school). The performances of the two models were evaluated using a test dataset that was not used for model learning. The LSTM model outperformed the ARIMA model in all households (correlation coefficient: mean 89% and root mean square error: mean 5.60 m3). Therefore, it is expected that the proposed model can predict customer-specific water consumption at the household level depending on the type of use.

Amenity counts significantly improve water consumption predictions

Anticipating the increase in water demand in an urban area requires us to properly understand daily human movement driven by population size, land use, and amenity types among others. Mobility data from phones can capture human movement, but not only is this hard to obtain, but it also does not tell where the population is going. Previous studies have shown that amenity types can be used to predict people’s movement patterns; thus, we propose using crowd-sourced amenity data and other open data sources as reasonable proxies for human mobility. Here we present a framework for predicting water consumption in areas with established service water connections and generalize it to underserved areas. Our work used features such as geography, population, and domestic consumption ratio and compared the prediction performance of various machine learning algorithms. We used 44 months of monthly water consumption data from January 2018 to July 2021, aggregated across 1790 district metering areas (DMAs) in the east service zone of Metro Manila. Results show that amenity counts reduce the mean absolute error (MAE) of predictions by 1,440 m³/month or as much as 5.73% compared to just using population and topology features. Predicted consumption during the pandemic also improved by as much as 1,447 m³/month or nearly 16% compared to just using population and topology features. We find that Gradient Boosting Trees are the best models to handle the data and feature set used in this work. Finally, the developed model is robust to disruptions in human mobility, such as lockdowns, indicating that amenities are sufficient to predict water consumption.

Revegetation of native desert plants enhances food security and water sustainability in arid regions: Integrated modeling assessment

Food security and water sustainability in arid and semiarid regions are threatened by rapid population growth, declining natural resources, and global climate change. Countries in the arid regions compensate meat import by raising domestic livestock with cultivated green fodder, which diminishes lands for other crops and depletes precious water resources. This study presents for the first time an in-depth integrated food water ecosystem (FWEco) nexus modeling on the feasibility of restoring 10% of Kuwait's desert as grazing rangeland to alleviate water consumption from fodder production. Our results showed that revegetating 10% of the country's land with native species could support up to 23% of domestic livestock through natural grazing at optimal coverage (70%) and high productivity, and decrease water consumption by up to 90%. However, depending solely on natural rainfall is unlikely to achieve the optimal coverage. Strategic supplemental irrigation in the fall season (e.g., October and November) is required to maximize vegetation coverage and enhance food security and water sustainability. Significantly, strategic irrigation results in much lower net water consumption because irrigating native species requires much less water than green fodder cultivation. Therefore, revegetating desert lands with native species to restore their natural grazing service can be a sustainable approach to simultaneously improve food security and water sustainability in arid landscapes.

Daily River Water Temperature Prediction: A Comparison between Neural Network and Stochastic Techniques

The temperature of river water (TRW) is an important factor in river ecosystem predictions. This study aims to compare two different types of numerical model for predicting daily TRW in the Warta River basin in Poland. The implemented models were of the stochastic type—Autoregressive (AR), Moving Average (MA), Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA)—and the artificial intelligence (AI) type—Adaptive Neuro Fuzzy Inference System (ANFIS), Radial Basis Function (RBF) and Group Method of Data Handling (GMDH). The ANFIS and RBF models had the most fitted outputs and the AR, ARMA and ARIMA patterns were the most accurate ones. The results showed that both of the model types can significantly present suitable predictions. The stochastic models have somewhat less error with respect to both the highest and lowest TRW deciles than the AIs and were found to be better for prediction studies, with the GMDH complex model in some cases reaching Root Mean Square Error (RMSE) = 0.619 °C and Nash-Sutcliff coefficient (NS) = 0.992, while the AR(2) simple linear model with just two inputs was partially able to achieve better results (RMSE = 0.606 °C and NS = 0.994). Due to these promising outcomes, it is suggested that this work be extended to other catchment areas to extend and generalize the results.

Application of LSTM Networks for Water Demand Prediction in Optimal Pump Control

Every morning, water suppliers need to define their pump schedules for the next 24 h for drinking water production. Plans must be designed in such a way that drinking water is always available and the amount of unused drinking water pumped into the network is reduced. Therefore, operators must accurately estimate the next day’s water consumption profile. In real-life applications with standard consumption profiles, some expert system or vector autoregressive models are used. Still, in recent years, significant improvements for time series prediction have been achieved through special deep learning algorithms called long short-term memory (LSTM) networks. This paper investigates the applicability of LSTM models for water demand prediction and optimal pump control and compares LSTMs against other methods currently used by water suppliers. It is shown that LSTMs outperform other methods since they can easily integrate additional information like the day of the week or national holidays. Furthermore, the online- and transfer-learning capabilities of the LSTMs are investigated. It is shown that LSTMs only need a couple of days of training data to achieve reasonable results. As the focus of the paper is on the real-world application of LSTMs, data from two different water distribution plants are used for benchmarking. Finally, it is shown that the LSTMs significantly outperform the system currently in operation.