A new Fuzzy-LOGIC based Model for Chlorophyll-a in Pulicat Lagoon, India (original) (raw)
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CHLfuzzy: a spreadsheet tool for the fuzzy modeling of chlorophyll concentrations in coastal lagoons
Hydrobiologia, 2008
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.
Modelling of Chlorophyll-A Concentrations in Pulicat Lagoon , Southeast Coast of India Using Artificial Neural Network, 2013
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.
Sustainability
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.
Use of AI to predict estuarine chlorophyll level.pdf
The time series analysis of chlorophyll a was carried out for more than 3 decades (1984-2018) from the coastal water of Digha and the data bank were subject to Nonlinear Autoregressive Neural Network Model to evaluate the status of the coastal water in 2050. The concentration of chlorophyll a ranged between 1.05 mgm-3 (in 2009) to 5.16 mgm-3 (in 1984) during the span of 35 years (real-time data). Chlorophyll a has a great role to drive the marine and estuarine food chain as it acts as the engine to transfer the energy derived from the Sun through different tires of the food chain. The decreasing trend of chlorophyll a with time is a warning signal for the fishery products from the region as the phytoplankton containing chlorophyll a serve as the major food of the fishes.
This study attempts to find the best suited chlorophyll estimation model for the coastal waters off Mumbai, situated on the western coast of India. These waters are a part of the Arabian Sea, highly productive with optical characteristics of case-2 type. There are several diffuse and point sources of domestic and industrial sewage effluent outlets along the coast, apart from two major marine outfalls located around 3.5 km into the sea. The study attempts to use MODIS data for the pre-monsoon season and test several exiting chlorophyll _ a estimation models for their site and season suitability for this area, with the help of synchronous sea-cruise data. The study area has been divided into various subgroups of ambient water quality difference, namely, highly sediment-laden water (outfall zones), high chlorophyll concentration zone and mixed water patches (high sediment + high chlorophyll concentration). The models are tested for each of these regions with the help of in-situ chlorophyll_a data, and their behaviour is analyzed through regression based analyses.