Water demand modelling using evolutionary computation techniques: integrating water equity and justice for realization of the sustainable development goals

Long-term water demand forecasting using artificial intelligence models in the Tuojiang River basin, China

Accurate forecasts of water demand are a crucial factor in the strategic planning and judicious use of finite water resources within a region, underpinning sustainable socio-economic development. This study aims to compare the applicability of various artificial intelligence models for long-term water demand forecasting across different water use sectors. We utilized the Tuojiang River basin in Sichuan Province as our case study, comparing the performance of five artificial intelligence models: Genetic Algorithm optimized Back Propagation Neural Network (GA-BP), Extreme Learning Machine (ELM), Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Random Forest (RF). These models were employed to predict water demand in the agricultural, industrial, domestic, and ecological sectors using actual water demand data and relevant influential factors from 2005 to 2020. Model performance was evaluated based on the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), with the most effective model used for 2025 water demand projections for each sector within the study area. Our findings reveal that the GPR model demonstrated superior results in predicting water demand for the agricultural, domestic, and ecological sectors, attaining R2 values of 0.9811, 0.9338, and 0.9142 for the respective test sets. Also, the GA-BP model performed optimally in predicting industrial water demand, with an R2 of 0.8580. The identified optimal prediction model provides a useful tool for future long-term water demand forecasting, promoting sustainable water resource management.

A Critical Review of Short-Term Water Demand Forecasting Tools—What Method Should I Use?

The challenge for city authorities goes beyond managing growing cities, since as cities develop, their exposure to climate change effects also increases. In this scenario, urban water supply is under unprecedented pressure, and the sustainable management of the water demand, in terms of practices including economic, social, environmental, production, and other fields, is becoming a must for utility managers and policy makers. To help tackle these challenges, this paper presents a well-timed review of predictive methods for short-term water demand. For this purpose, over 100 articles were selected from the articles published in water demand forecasting from 2010 to 2021 and classified upon the methods they use. In principle, the results show that traditional time series methods and artificial neural networks are among the most widely used methods in the literature, used in 25% and 20% of the articles in this review. However, the ultimate goal of the current work goes further, providing a comprehensive guideline for engineers and practitioners on selecting a forecasting method to use among the plethora of available options. The overall document results in an innovative reference tool, ready to support demand-informed decision making for disruptive technologies such as those coming from the Internet of Things and cyber–physical systems, as well as from the use of digital twin models of water infrastructure. On top of this, this paper includes a thorough review of how sustainable management objectives have evolved in a new era of technological developments, transforming data acquisition and treatment.

Photobiomodulation in diabetic wound healing: A review of red and near-infrared wavelength applications

The development of a painless, non-invasive, and faster way to diabetic wound healing is at the forefront of research. The complexity associated with diabetic wounds makes it a cause for concern amongst diabetic patients and the world at large. Irradiation of cells generates a photobiomodulatory response on cells and tissues, directly causing alteration of cellular processes and inducing diabetic wound repair. Photobiomodulation therapy (PBMT) using red and near-infrared (NIR) wavelengths is being considered as a promising technique for speeding up the rate of diabetic wound healing, eradication of pain and reduction of inflammation through the alteration of diverse cellular and molecular processes. This review presents the extent to which the potential of red and NIR wavelengths have been harnessed in PBMT for diabetic wound healing. Important research challenges and gaps are identified and discussed, and future directions mapped out. This review thus provides useful insights and strategies into improvement of PBMT, including its acceptance within the global medical research community.