Application of Fuzzy Indices to determine the trophic status of Pulicat Lagoon, Southeast Coast of India (original) (raw)

CHLFuzzy is a user-friendly, flexible, multiple-input single-output Takagi-Sugeno fuzzy rule based model developed in a MS-Excel® spreadsheet environment. The model receives a raw dataset consisting of four predictor variables, e.g., water temperature, dissolved oxygen content, dissolved inorganic nitrogen concentration, and solar radiation levels. It then defines fuzzy sets according to a collection of fuzzy membership functions, allowing for the establishment of fuzzy ‘if–then’ rules, and predicts chlorophyll-a concentrations, which highly compare to the measured ones. The performance of the model was tested against the Adaptive Neural Fuzzy Inference System (ANFIS), showing satisfactory results. An extensive dataset of environmental observations in Vassova Lagoon (Northern Greece), during the years 2001–2002, was used to train the model and an independent dataset collected during 2004 was used to validate CHLFuzzy and ANFIS models. Although both models showed a similar performance on the training dataset, with quite satisfactory agreement between observed and modeled chlorophyll-a values, the best results were obtained using the CHLfuzzy model. Similarly, the CHLfuzzy model depicted a fairly good ability to hindcast chlorophyll-a concentrations for the verification dataset, thus improving ANFIS model forecasts. Overall results suggest that CHLfuzzy can potentially be used as a lagoon water quality forecasting tool requiring limited computational cost.

Formations of algal blooms increasingly pollute both salt and fresh water ecosystems throughout the world. Owing to its negative impacts on human health and aquatic life, this widely reported phenomenon has become a serious environmental problem. While many process-based, statistical and empirical models exist for water quality prediction, Artificial Neural Network (ANN) models are increasingly being used for water related applications as they are often capable of model- ling complex systems for which behavioural rules or underlying physical processes are either unknown or difficult to simulate. In the present study, a feed forward neural network is proposed to model the primary productivity of Pulicat lagoon. The commonly used back propagation learning algorithm has been employed for training the ANN. The model was constructed using five years of seasonal data set on the mouth part of Pulicat lagoon which is the most dynamic part of the lagoon. Despite the very complex and peculiar nature of this region of the lagoon, a very good correlation (R = 0.998) was observed between the measured and predicted values dur- ing model validation. The Mean Square Error between the measured and predicted values was found to be 0.018. Thus, the resulting prediction of Chlorophyll-a values clearly indicated that ANNs can fit the complexity and nonlinearity of ecological phenomena such as phytoplankton production to a high degree.

As a variable system, the Lake of Kastoria is a good example regarding the pattern of the Mediterranean shallow lakes. The focus of this study is on the investigation of this lake’s eutrophication, analyzing the relation of the basic factors that affect this phenomenon using fuzzy logic. In the method we suggest, while there are many fuzzy implications that can be used since the proposition can take values in the close interval [0,1], we investigate the most appropriate implication for the studied water body. We propose a method evaluating fuzzy implications by constructing triangular non-asymptotic fuzzy numbers for each of the studied parameters coming from experimental data. This is achieved with the use of fuzzy estimators and fuzzy linear regression. In this way, we achieve a better understanding of the mechanisms and functions that regulate this ecosystem.

The complexity of evaluating the quality of an aquatic environment with its numerous variables has resulted in several quality indexes to synthesize all information in a single value. However, most of these indexes are based on few environmental variables, losing information from other relevant variables. This article presents a new model capable of representing the quality level of a tropical oligo-mesotrophic reservoir on a numerical scale, considering the subjectivity implicit in the concept of quality, and involving several physical, chemical and biological variables. The proposed model, called "Fuzzy Indices for Water Quality Assessment and Biotics" (FUZZY-WBQAI), is based on fuzzy inference systems, providing a way to deal with the uncertainty between the quality categories. A computational tool was developed, which automatically assesses the quality of water, considering different methodologies that depend on the stratification conditions and the longitudinal zone of the reservoir. The model calculates two indices: one for water quality and one biotic that uses metrics from the fish assembly. The model was effective in revealing the decrease in water quality in summer months with higher temperature and precipitation. The effect of the mixture of the stratified reservoir in winter in decreasing the water quality in an area of fish cultivation was highlighted. The biotic index was sensitive to spatial changes in the environmental quality of the reservoir. The results were considered satisfactory, in agreement with the specialized knowledge, and can provide a rapid diagnosis of water conditions in oligo-mesotrophic reservoirs in the tropics.

A Mamdani-type fuzzy-logic model has been developed to link Mediterranean seagrass 7 abundance to the prevailing environmental conditions. Big Databases, as UNEP-WCMC (seagrass 8 abundance), CMEMS and EMODnet (oceanographic/environmental) and human-impact 9 parameters were utilized for this expert system. Model structure and input parameters were tested 10 according to their capacity to accurately predict seagrass families at specific locations. The 11 optimum FIS comprised of four input variables: water depth, sea surface temperature and nitrates 12 and bottom chlorophyll-a concentration, exhibiting fair accuracy (76%). Results illustrated that 13 Posidoniaceae prefers cool (16-18oC) and low chlorophyll-a presence (< 0.2 mg/m3); Zosteraceae 14 favors cool (16-18oC) and mesotrophic waters (Chl-a > 0.2 mg/m3), but also slightly warmer 15 (18-19.5 oC) with lower Chl-a levels (< 0.2 mg/m3); Cymodoceaceae lives from warm, oligotrophic 16 (19.5-21.0oC and Chl-a < 0.3 mg/m3) t...