Mixing and bimolecular reaction kinetics in heterogeneous porous media flows

Ph.D.Vivek Kapoo

Classification and Prediction of Fecal Coliform in Stream Waters Using Decision Trees (DTs) for Upper Green River Watershed, Kentucky, USA

The classification of stream waters using parameters such as fecal coliforms into the classes of body contact and recreation, fishing and boating, domestic utilization, and danger itself is a significant practical problem of water quality prediction worldwide. Various statistical and causal approaches are used routinely to solve the problem from a causal modeling perspective. However, a transparent process in the form of Decision Trees is used to shed more light on the structure of input variables such as climate and land use in predicting the stream water quality in the current paper. The Decision Tree algorithms such as classification and regression tree (CART), iterative dichotomiser (ID3), random forest (RF), and ensemble methods such as bagging and boosting are applied to predict and classify the unknown stream water quality behavior from the input variables. The variants of bagging and boosting have also been looked at for more effective modeling results. Although the Random Forest, Gradient Boosting, and Extremely Randomized Tree models have been found to yield consistent classification results, DTs with Adaptive Boosting and Bagging gave the best testing accuracies out of all the attempted modeling approaches for the classification of Fecal Coliforms in the Upper Green River watershed, Kentucky, USA. Separately, a discussion of the Decision Support System (DSS) that uses Decision Tree Classifier (DTC) is provided

Trophic status estimation of case-2 water bodies of the Godavari River basin using satellite imagery and artificial neural network (ANN)

The dynamics of trophic status estimation of case-2 water bodies on a synoptic mode for frequent intervals is essential for water quality management. The present study attempts to develop trophic status estimation approaches utilizing Landsat-8 and Sentinel-2 images as inputs. The chlorophyll-a concentration, a proxy parameter for trophic status, was estimated using the empirical method, fluorescence line height (FLH) method, and artificial neural network (ANN) approaches using spectral reflectance values as inputs. The outcomes following the empirical approaches revealed the scope of kernel normalized difference vegetation index (kNDVI) (R2 = 0.85; RMSE = 2 μg/l) for estimating the chlorophyll-a concentration using Sentinel-2 images of the Godavari River basin. Though the performance of the FLH method (R2 = 0.91; RMSE = 1.6 μg/l) was superior to kNDVI-based estimation, it lacks the capability to estimate chlorophyll-a concentration above 20 μg/l. Due to the existence of eutrophic regions within the Godavari basin (28%), adopting better approaches like ANN for trophic status estimation is essential. To accomplish the same, the Levenberg–Marquardt algorithm-based ANN was developed using non-redundant bands of Sentinel-2 as inputs, and Sentinel-3 derived chlorophyll-a values as output. The developed architecture was successful in estimating trophic status estimations at all levels. HIGHLIGHTS Sentinel-2 performed better than Landsat-8 for trophic status estimations.; Sentinel-2 derived kNDVI for chlorophyll-a concentration of case-2 water bodies.; FLH method for estimating chlorophyll-a up to mesotrophic level.; Prospectus of Sentinel-3 generated chlorophyll-a for estimating the trophic status.; Sentinel-2 band values as inputs and Sentinel-3 chlorophyll-a values as output of ANN for trophic status estimations.

Prediction of stream water quality in Godavari River Basin, India using statistical and artificial neural network models

The successful prediction of the stream or river water quality is gaining the attention of various governmental agencies, and pollution control boards worldwide due to its useful applications in determining watershed health, biodiversity, ecology, and suitability of potable water needs of the river basin. The physically based computational water quality models would require large spatial and temporal information databases of climatic, hydrologic, and environmental variables and solutions of nonlinear, partial differential equations at each grid point in a river basin. These models suffer from estimability, convergence, stability, approximation, dispersion, and consistency issues. In such a problematic modeling scenario, an artificial neural network (ANN) modeling of 22 stream water quality parameters (SWQPs) is performed from easily measurable data of precipitation, temperature, and novel land use parameters obtained from Geographic Information System (GIS) analysis for the Godavari River Basin, India. The ANN models are compared with the more traditional, statistical linear, and nonlinear regression models for accuracy and performance statistics. This study obtains regression coefficients of 0.93, 0.78, 0.83, and 0.74 for electrical conductivity, dissolved oxygen, biochemical oxygen demand, and nitrate in testing using feedforward ANNs compared with a maximum of 0.45 using linear and nonlinear regressions. Principal component analysis (PCA) is performed to reduce the input data dimension. The subsequent modeling using radial basis function and ANNs is found to improve the overall regression coefficients slightly for the chosen four water quality parameters (WQPs). A closed form equation for electrical conductivity has been derived from MATLAB simulations. The successful modeling results indicate the effectiveness and potential of ANNs over the statistical regression approaches for estimating the highly nonlinear problem of stream water quality distributions. HIGHLIGHTS A GIS, ANN-based causal WQ model is developed for a non-Karst watershed.; Novel land use factors are developed for the model.; PCA-based ANN models are found to be superior compared with others.; An equation for conductivity is developed from MATLAB simulations.; Land use parameters are also important along with climate parameters for water quality model development.