A Fuzzy Logic Model for Early Warning of Algal Blooms in a Tidal-Influenced River

Current status and prospects of algal bloom early warning technologies: A Review

In recent years, frequent occurrences of algal blooms due to environmental changes have posed significant threats to the environment and human health. This paper analyzes the reasons of algal bloom from the perspective of environmental factors such as nutrients, temperature, light, hydrodynamics factors and others. Various commonly used algal bloom monitoring methods are discussed, including traditional field monitoring methods, remote sensing techniques, molecular biology-based monitoring techniques, and sensor-based real-time monitoring techniques. The advantages and limitations of each method are summarized. Existing algal bloom prediction models, including traditional models and machine learning (ML) models, are introduced. Support Vector Machine (SVM), deep learning (DL), and other ML models are discussed in detail, along with their strengths and weaknesses. Finally, this paper provides an outlook on the future development of algal bloom warning techniques, proposing to combine various monitoring methods and prediction models to establish a multi-level and multi-perspective algal bloom monitoring system, further improving the accuracy and timeliness of early warning, and providing more effective safeguards for environmental protection and human health.

Modeling, challenges, and strategies for understanding impacts of climate extremes (droughts and floods) on water quality in Asia: A review

The increasing frequency and intensity of extreme climate events are among the most expected and recognized consequences of climate change. Prediction of water quality parameters becomes more challenging with these extremes since water quality is strongly related to hydro-meteorological conditions and is particularly sensitive to climate change. The evidence linking the influence of hydro-meteorological factors on water quality provides insights into future climatic extremes. Despite recent breakthroughs in water quality modeling and evaluations of climate change's impact on water quality, climate extreme informed water quality modeling methodologies remain restricted. This review aims to summarize the causal mechanisms across climate extremes considering water quality parameters and Asian water quality modeling methods associated with climate extremes, such as floods and droughts. In this review, we (1) identify current scientific approaches to water quality modeling and prediction in the context of flood and drought assessment, (2) discuss the challenges and impediments, and (3) propose potential solutions to these challenges to improve understanding of the impact of climate extremes on water quality and mitigate their negative impacts. This study emphasizes that one crucial step toward enhancing our aquatic ecosystems is by comprehending the connections between climate extreme events and water quality through collective efforts. The connections between the climate indices and water quality indicators were demonstrated to better understand the link between climate extremes and water quality for a selected watershed basin.

A Study of Assessment and Prediction of Water Quality Index Using Fuzzy Logic and ANN Models

Various human activities have been the main causes of surface water pollution. The uneven distribution of industrial enterprises in the territories of the main river basins of Ukraine do not always allow the real state of the water quality to be assessed. This article has three purposes: (1) the modification of the Ukrainian method for assessing the WQI, taking into account the level of negative impact of the most dangerous chemical elements, (2) the modeling of WQI assessment using fuzzy logic and (3) the creation of an artificial neural network model for the prediction of the WQI. The fuzzy logic model used four input variables and calculated one output variable (WQI). In the final stage of the study, six ANN models were analyzed, which differed from each other in various loss function optimizers and activation functions. The optimal results were shown using an ANN with the softmax activation function and Adam’s loss function optimizer (MAPE = 9.6%; R2 = 0.964). A comparison of the MAPE and R2 indicators of the created ANN model with other models for assessing water quality showed that the level of agreement between the forecast and target data is satisfactory. The novelty of this study is in the proposal to modify the WQI assessment methodology which is used in Ukraine. At the same time, the phased and joint use of mathematical tools such as the fuzzy logic method and the ANN allow one to effectively evaluate and predict WQI values, respectively.

Water Supply Management Index: Leon, Guanajuato, Mexico

In order to guarantee the sustainability of the potable water supply service, a water utility must generate improvements in its performance in an integrated manner. The objective of this research is to analyze and provide information about the components and indicators used by the water utility of Leon, Guanajuato, Mexico, which directly impact water management. The Analytic Hierarchy Process (AHP) and Fuzzy Logic (FL) methodologies were applied. The study determined the trends and evolution over time from 2002 to 2017. From the combination of both methodologies, a Water Supply Management Index was obtained with an average value of 0.79, which shows positive progress for water resource management by the water utility. However, the traditional indicators are insufficient and require particular attention. The analysis helped to identify those indicators that do impact water management and their ability to measure the sustainability of the city’s water utility. This may make it possible to monitor the progress toward the accomplishment of Sustainable Development Goal 6 (SDG 6), by performing an evaluation and thorough analysis of the status of water resources.