Efficiency of coupled invasive weed optimization-adaptive neuro fuzzy inference system method to assess physical habitats in streams

This study presents a coupled invasive weed optimization-adaptive neuro fuzzy inference system method to simulate physical habitat in streams. We implement proposed method in Lar national park in Iran as one of the habitats of Brown trout in southern Caspian Sea basin. Five indices consisting of root mean square error (RMSE), mean absolute error (MAE), reliability index, vulnerability index and Nash–Sutcliffe model efficiency coefficient (NSE) are utilized to compare observed fish habitats and simulated fish habitats. Based on results, measurement indices demonstrate model is robust to assess physical habitats in rivers. RMSE and MAE are 0.09 and 0.08 respectively. Besides, NSE is 0.78 that indicates robustness of model. Moreover, it is necessary to apply developed habitat model in a practical habitat simulation. We utilize two-dimensional hydraulic model in steady state to simulate depth and velocity distribution. Based on qualitative comparison between results of model and observation, coupled invasive weed optimization-adaptive neuro fuzzy inference system method is robust and reliable to simulate physical habitats. We recommend utilizing proposed model for physical habitat simulation in streams for future studies

Linking SVM based habitat model and evolutionary optimisation for managing environmental impacts of hydropower plants

The present study proposes a support vector machine (SVM)-based habitat model linked with evolutionary optimisation to balance the impacts of generating hydropower on the downstream river habitats. This method was applied in the Rajaei reservoir and Tajan River basin in Iran to mitigate the environmental impacts of hydropower plants. SVM model classified the habitat suitability at downstream river in which a sigmoid function considering different slopes was applied. The Nash–Sutcliffe efficiency coefficient as the evaluation index of the habitat model is 0.8, which implies the SVM model is robust to simulate physical habitats. Hydraulic simulation demonstrated that depth and velocity change from zero to 1.79 m and zero to 1.82 m/s, respectively. Most suitable river flow is 7 m3/s downstream of Rajaei reservoir. Five evolutionary algorithms were used to balance environmental impacts with generating hydropower. Finally, a fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) selected the best optimal solution in the Rajaei reservoir. Based on optimisation results, The simulated annealing (SA) algorithm was the best optimisation method to balance generating hydropower and downstream ecological impacts, in which average habitat suitability is more than 90% of average habitat suitability in the natural flow, while reliability of generating hydropower is 38%. Moreover, SA is able to minimise the average difference between habitat suitability in the optimal release and the natural flow properly. Using the proposed method is recommendable to mitigate the potential impacts of generating hydropower on the downstream river habitats

An optimization approach for managing environmental impacts of generating hydropower on fish biodiversity

The present study proposes an applicable framework to mitigate the impacts of generating hydropower on the fish biodiversity in the large reservoirs considering water quality and hydraulic factors. Moreover, several data driven models were utilized for simulating effective parameters. Finally, a multivariate linear regression model was used to estimate the fish biodiversity index in which two combined hydraulic and water quality indices were the inputs of the model and the fish biodiversity index was the output of the model. Then, all the simulators were applied in the structure of the hydropower plant operation optimization for different hydrological conditions (i.e. dry years, normal years and wet years) in which two purposes were defined: 1- minimizing the fish biodiversity loss 2- minimizing the loss of generating hydropower. Based on the results in the case study, all the simulators are reliable to model the physical flow, water quality parameters and the fish biodiversity index. The optimization model is able to minimize the impacts on the fish biodiversity properly. The reliability of generating hydropower in the dry years is 30%, while it is 53% in the wet years. High computational complexities might be a limitation for the model

Optimal operation of dams/reservoirs emphasizing potential environmental and climate change impacts

Mahdi studied the potential ecological and climate change impacts on management of dams. He developed several new optimization frameworks in which benefits of dams are maximized, while above impacts are mitigated. Governments and consulting engineers can use the proposed frameworks for managing dams considering environmental challenges in river basins

A hydro-environmental optimization for assessing sustainable carrying capacity

The present study proposes an applicable method to determine the population carrying capacity of urban areas in which ecological impacts of river ecosystem as the source of water supply and sustainable population growth are linked. A multiobejctive optimization method was developed in which two objectives were considered: 1) minimizing the fish population loss as the environmental index of the river ecosystem and 2) minimizing the difference between initial population carrying capacity and the sustainable population carrying capacity. The ecological impacts of the river ecosystem were assessed through the potential fish population as an environmental index using several artificial intelligence and regression models. Based on case study results, the initial plan of development is not reliable because ecological impacts on the river ecosystem are remarkable. The proposed method is able to reduce the ecological impacts. However, the sustainable population carrying capacity is considerably lower than the initial planned population. It is needed to reduce the planned population more than 45% in the case study. Habitat loss is less than 35% which means the optimization model is able to find an optimal solution for balancing environmental requirements and humans’ needs. In other words, the optimization model balances the needs of environment and water supply by reducing 45% of population and decreasing habitat loss to 35%

Creating Virtual Animals Through Machine Learning

Approximately 42 percent of threatened or endangered species are at risk due to invasive species. Some invasive species find the new habitat by themselves during migrations, others are misplaced by humans, be it by mistake or necessity. This project aims to create a virtual habitat, populated by intelligent agents that represent the animals present in it. Programmers and scientists can add invasive species, and simulate what might happen. This will allow a more proactive response to this type of crisis. Different data-driven models are explored in order to find the best one for the problem at hands. Game engines are discussed, they have improved greatly over the last decade, and are accessible to everyone. Reliable tools to build simple or complex prototypes that give us graphic representations that can be photo realisticAproximadamente 42 por cento das espécies em vias de extinção estão em risco devido a espécies invasoras. Algumas dessas espécies invasoras chegam aos novos habitats através de migrações, outras chegam através da mão humana, voluntaria ou involuntariamente. Este projeto tem como objetivo criar um habitat virtual, com agentes inteligentes que rep resentam os animais presentes nesse mesmo habitat. Programadores e cientistas poderão adicionar espécies invasoras, e simular o que pode acontecer. Isto irá permitir uma resposta mais proativa quando estes tipos de crises acontecem. Diferentes modelos orientados a dados são explorados, a fim de perceber qual será o melhor para resolver o problema. Game engines são discutidos, este tipo de ferramenta tem evoluído bastante ao longo da última década, são ferramentas grátis, que podem ser usadas para criar protótipos com gráficos simples, ou foto realista

On the development of decision-making systems based on fuzzy models to assess water quality in rivers

There are many situations where a linguistic description of complex phenomena allows better assessments. It is well known that the assessment of water quality continues depending heavily upon subjective judgments and interpretation, despite the huge datasets available nowadays. In that sense, the aim of this study has been to introduce intelligent linguistic operations to analyze databases, and produce self interpretable water quality indicators, which tolerate both imprecision and linguistic uncertainty. Such imprecision typically reflects the ambiguity of human thinking when perceptions need to be expressed. Environmental management concepts such as: "water quality", "level of risk", or "ecological status" are ideally dealt with linguistic variables. In the present Thesis, the flexibility of computing with words offered by fuzzy logic has been considered in these management issues. Firstly, a multipurpose hierarchical water quality index has been designed with fuzzy reasoning. It integrates a wide set of indicators including: organic pollution, nutrients, pathogens, physicochemical macro-variables, and priority micro-contaminants. Likewise, the relative importance of the water quality indicators has been dealt with the analytic hierarchy process, a decision-aiding method. Secondly, a methodology based on a hybrid approach that combines fuzzy inference systems and artificial neural networks has been used to classify ecological status in surface waters according to the Water Framework Directive. This methodology has allowed dealing efficiently with the non-linearity and subjective nature of variables involved in this classification problem. The complexity of inference systems, the appropriate choice of linguistic rules, and the influence of the functions that transform numerical variables into linguistic variables have been studied. Thirdly, a concurrent neuro-fuzzy model based on screening ecological risk assessment has been developed. It has considered the presence of hazardous substances in rivers, and incorporates an innovative ranking and scoring system, based on a self-organizing map, to account for the likely ecological hazards posed by the presence of chemical substances in freshwater ecosystems. Hazard factors are combined with environmental concentrations within fuzzy inference systems to compute ecological risk potentials under linguistic uncertainty. The estimation of ecological risk potentials allows identifying those substances requiring stricter controls and further rigorous risk assessment. Likewise, the aggregation of ecological risk potentials, by means of empirical cumulative distribution functions, has allowed estimating changes in water quality over time. The neuro-fuzzy approach has been validated by comparison with biological monitoring. Finally, a hierarchical fuzzy inference system to deal with sediment based ecological risk assessment has been designed. The study was centered in sediments, since they produce complementary findings to water quality analysis, especially when temporal trends are required. Results from chemical and eco-toxicological analyses have been used as inputs to two parallel inference systems which assess levels of contamination and toxicity, respectively. Results from both inference engines are then treated in a third inference engine which provides a final risk characterization, where the risk is provided in linguistic terms, with their respective degrees of certitude. Inputs to the risk system have been the levels of potentially toxic substances, mainly metals and chlorinated organic compounds, and the toxicity measured with a screening test which uses the photo-luminescent bacteria Vibrio fischeri. The Ebro river basin has been selected as case study, although the methodologies here explained can easily be applied to other rivers. In conclusion, this study has broadly demonstrated that the design of water quality indexes, based on fuzzy logic, emerges as suitable and alternative tool to support decision makers involved in effective sustainable river basin management plans.Existen diversas situaciones en las cuales la descripción en términos lingüísticos de fenómenos complejos permite mejores resultados. A pesar de los volúmenes de información cuantitativa que se manejan actualmente, es bien sabido que la gestión de la calidad del agua todavía obedece a juicios subjetivos y de interpretación de los expertos. Por tanto, el reto en este trabajo ha sido la introducción de operaciones lógicas que computen con palabras durante el análisis de los datos, para la elaboración de indicadores auto-interpretables de calidad del agua, que toleren la imprecisión e incertidumbre lingüística. Esta imprecisión típicamente refleja la ambigüedad del pensamiento humano para expresar percepciones. De allí que las variables lingüísticas se presenten como muy atractivas para el manejo de conceptos de la gestión medioambiental, como es el caso de la "calidad del agua", el "nivel de riesgo" o el "estado ecológico". Por tanto, en la presente Tesis, la flexibilidad de la lógica difusa para computar con palabras se ha adaptado a diversos tópicos en la gestión de la calidad del agua. Primero, se desarrolló un índice jerárquico multipropósito de calidad del agua que se obtuvo mediante razonamiento difuso. El índice integra un extenso grupo de indicadores que incluyen: contaminación orgánica, nutrientes, patógenos, variables macroscópicas, así como sustancias prioritarias micro-contaminantes. La importancia relativa de los indicadores al interior del sistema de inferencia se estimó con un método de análisis de decisiones, llamado proceso jerárquico analítico. En una segunda fase, se utilizó una metodología híbrida que combina los sistemas de inferencia difusos y las redes neuronales artificiales, conocida como neuro-fuzzy, para el estudio de la clasificación del estado ecológico de los ríos, de acuerdo con los lineamientos de la Directiva Marco de Aguas. Esta metodología permitió un manejo adecuado de la no-linealidad y naturaleza subjetiva de las variables involucradas en este problema clasificatorio. Con ella, se estudió la complejidad de los sistemas de inferencia, la selección apropiada de reglas lingüísticas y la influencia de las funciones que transforman las variables numéricas en lingüísticas. En una tercera fase, se desarrolló un modelo conceptual neuro-fuzzy concurrente basado en la metodología de evaluación de riesgo ecológico preliminar. Este modelo consideró la presencia de sustancias peligrosas en los ríos, e incorporó un mapa auto-organizativo para clasificar las sustancias químicas, en términos de su peligrosidad hacia los ecosistemas acuáticos. Con este modelo se estimaron potenciales de riesgo ecológico por combinación de factores de peligrosidad y de concentraciones de las sustancias químicas en el agua. Debido a la alta imprecisión e incertidumbre lingüística, estos potenciales se obtuvieron mediante sistemas de inferencia difusos, y se integraron por medio de distribuciones empíricas acumuladas, con las cuales se pueden analizar cambios espacio-temporales en la calidad del agua. Finalmente, se diseñó un sistema jerárquico de inferencia difuso para la evaluación del riesgo ecológico en sedimentos de ribera. Este sistema estima los grados de contaminación, toxicidad y riesgo en los sedimentos en términos lingüísticos, con sus respectivos niveles de certeza. El sistema se alimenta con información proveniente de análisis químicos, que detectan la presencia de sustancias micro-contaminantes, y de ensayos eco-toxicológicos tipo "screening" que usan la bacteria Vibrio fischeri. Como caso de estudio se seleccionó la cuenca del río Ebro, aunque las metodologías aquí desarrolladas pueden aplicarse fácilmente a otros ríos. En conclusión, este trabajo demuestra ampliamente que el diseño y aplicación de indicadores de calidad de las aguas, basados en la metodología de la lógica difusa, constituyen una herramienta sencilla y útil para los tomadores de decisiones encargados de la gestión sostenible de las cuencas hidrográficas

Developing magnetic functionalized multi-walled carbon nanotubes-based buckypaper for the removal of Furazolid

Magnetic f-MWCNTs-based BP/PVA membrane was fabricated and utilized for the elimination of furazolidone (FZD) from aqueous solution. Characterisation and adsorption studies were performed to evaluate the performance and adsorptive efficiency, respectively of the membrane. Furthermore, statistical and machine learning technique were also applied to predict the removal efficiency of FZD on the membrane. The results revealed that magnetic f-MWCNTs-based BP/PVA membrane has the potential to be used as an efficient membrane for practical applications

Appropriate Wisdom, Technology, and Management toward Environmental Sustainability for Development

The protection and maintenance of environmental resources for future generations require responsible interaction between humans and the environment in order to avoid wasting natural resources. According to an ancient Native American proverb, “We do not inherit the Earth from our ancestors; we borrow it from our children.” This indigenous wisdom has the potential to play a significant role in defining environmental sustainability. Recent technological advances could sustain humankind and allow for comfortable living. However, not all of these advancements have the potential to protect the environment for future generations. Developing societies and maintaining the sustainability of the ecosystem require appropriate wisdom, technology, and management collaboration. This book is a collection of 19 important articles (15 research articles, 3 review papers, and 1 editorial) that were published in the Special Issue of the journal Sustainability entitled “Appropriate Wisdom, Technology, and Management toward Environmental Sustainability for Development” during 2021-2022.addresses the policymakers and decision-makers who are willing to develop societies that practice environmental sustainability, by collecting the most recent contributions on the appropriate wisdom, technology, and management regarding the different aspects of a community that can retain environmental sustainability

Predicting future coastal land use/cover change and associated sea-level impact on habitat quality in the Northwestern Coastline of Guinea-Bissau

The assessment of coastal land use/cover (LULC) change is one of the most precise techniques for detecting spatio-temporal change in the coastal system. This study, integrated Land Change Modeler, Habitat Quality Model, and Digital Shoreline Analysis System, to quantify spacio-temporal coastal LULC change and driving forces between 2000 and 2020. Combined the CA-Markov Model with Sea Level Affecting Marshes Model (SLAMM), merged local SLR data with future representative concentration pathway (RCP8.5) scenarios, and predicted future coastal LULC change and associated sea-level rise (SLR) impact on the coastal land use and habitat quality in short-, medium- and long-term. The study area had significant coastal LULC change between 2000 and 2020. The tidal flats, whose change was driven mainly by sea level, registered a total net gain of 57.93 km2 . We also observed the significant loss of developed land whose change was influenced by tidal flat with a total loss of − 75.58 km2. The tidal flat will experience a stunning net gain of 80.55 km2 between 2020 and 2060, making developed land the most negatively impacted land in the study area. The study led to the conclusion that the uncontrolled conversion of saltmarshes, mixed-forest, and mangroves into agriculture and infrastructures were the main factors affecting the coastal systems, including the faster coastal erosion and accretion observed during a 20-year period. The study also concluded that a low coastal elevation of − 1 m and a slope of less than 2◦have contributed to coastal change. Unprecedented changes will unavoidably pose a danger to coastal ecological services, socioeconomic growth, and food security. Timely efforts should be made by establishing sustainable mitigation methods to avoid the future impact.info:eu-repo/semantics/publishedVersio