Browsing by Author "Surendra, H.J."

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Application of Mamdani model-based fuzzy inference system in water consumption estimation using time series
(Springer Science and Business Media Deutschland GmbH, 2022) Surendra, H.J.; Deka, P.C.; Rajakumara, H.N.
Artificial intelligence methods resemble human thinking structure that are used in hydrological modeling. In this work, water consumption estimation modeling is done using Mamdani fuzzy inference system. Different combinations of the models were developed by changing structures scenario such as: membership function, rules criteria, fuzzy set and defuzzification method. Mapping of input and output function are done using climatic variables and water consumption data. Rainfall, maximum temperature, minimum temperature and relative humidity were used as input factors and water consumption as output function. The reasoning mechanism of the fuzzy inference system calculates the recommended value of water consumption. Obtained value is compared with the actual recommended values to determine the usefulness of the system. The performances of the models were evaluated using performance indices such as correlation coefficient, mean square error and mean relative error. Results highlight that Mamdani fuzzy inference system is effective in actual application. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Fuzzy and improved fuzzy-wavelet approach in modeling municipal residential water consumption estimation using climatic variables
(Springer, 2020) Surendra, H.J.; Deka, P.C.
This work highlights the importance of fuzzy-wavelet denoise and fuzzy-wavelet compress in modeling the municipal residential water consumption estimation. To begin, fuzzy logic is used with different rules, membership criteria and fuzzy set. Based on accuracy of the developed model, optimum number of rules and best membership function were selected. To improve the accuracy of the single fuzzy model, wavelets technique (denoise and compress approach) was coupled with fuzzy logic and results were compared to single fuzzy technique. To map the input and output functions, the present research work includes Mamdani fuzzy inference approach based on various climatic input variables like rainfall, maximum temperature, minimum temperature and relative humidity. The models were trained based on climatic data to a certain period, and corresponding estimated models were tested for the same period. Result highlights that models with denoise and compress approach have better accuracy compared to single fuzzy model. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
Municipal Residential Water Consumption Estimation Techniques Using Traditional and Soft Computing Approach: a Review
(Springer Science and Business Media B.V., 2022) Surendra, H.J.; Deka, P.C.
Rate of urbanization in towns and cities is so rapid, which leads to increase in the demand of water. Any developmental activities depend on availability of water. It is necessary to serve residents with better urban infrastructure by balancing both supply and demand. Hence, identifying the factors influencing consumption of water in residential area is very important. In this paper, climatic variables and socio-economic factors influencing water consumption and its computing techniques are discussed. Many studies showed that soft computing models are excellent models for predictions; however, they have been exiguously reported. Hence, here the advantages of soft computing methods are discussed and compared with traditional methods. In the present study, soft computing techniques and its applications such as fuzzy logic (FL), artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and different wavelets are summarized and reviewed from problem point of view and application point of view. Several information on the possible future research, application and combinations using soft computing methods were discussed. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.