• RADHIKA KARSHANBHAI HIRAPRA
• SANJAY SINGH
• TANMAY RATHOD

Several techniques such as; Fuzzy Inference System (FIS) and Neural Network (NN) are used for developing of the predictive models to estimate parameters of water quality. The main objective of this study is to compare between the predictive ability of the Artificial Neural Network (ANN) model and Adaptive Neuro-Fuzzy Inference System (ANFIS) model to estimate the Dissolved Oxygen(DO) using data from the sampling sites station at Billimora-amalsad Road on Ambika River in Gujarat, India. The data is obtained from the Gujarat pollution control board(GPCB), during 2010-2017. Total Ten parameters of water quality namely Dissolved Oxygen, Biochemical Oxygen Demand, Chemical Oxygen Demand ,ph, Nitrite, Nitrate, Phosphate, Total Dissolved Solids, Calcium, Magnesium are used to developed the models. The experimental results indicate that the ANFIS model provides a higher correlation coefficient (R2=0.885) in Training and (R2=0.818) in Testing with a lower root mean square error (RMSE=0.2307) in training and (RMSE= 0.3574) in Training than the corresponding (RMSE= 0.3863) ANN model

ANN, ANFIS, RIVER WATER, DO, WATER QUALITY

"“PREDICTION OF WATER QUALITY PARAMETER OF AMBIKA RIVER BY ARTIFICIAL INTELLIGENCE BASED MODELS”", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 4, page no.439-445, April-2018, Available :http://www.jetir.org/papers/JETIR1804085.pdf

"“PREDICTION OF WATER QUALITY PARAMETER OF AMBIKA RIVER BY ARTIFICIAL INTELLIGENCE BASED MODELS”", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 4, page no. pp439-445, April-2018, Available at : http://www.jetir.org/papers/JETIR1804085.pdf