An experimental database of kinematic viscosity of pure biodiesel was used for developing of models, where the input variables in the network were the temperature, the number of carbon atoms (NC) and the number of hydrogen atoms (NH) of the composition of methyl esters (C8:0, C10:0, C12:0, C14:0, C16:0, C16:1, C18:0, C18:1, C18:2, C18:3, C20:0, C20:1, C22:0, C22:1, C24:0 were considered as input variables on the ANFIS and ANN. This study investigates the applicability of adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) approaches for modeling the fatty acid methyl esters (FAMEs) property including kinematic viscosity at various temperatures and the volume fractions of biodiesel. A proper rainwater harvesting and management program is an appropriate option for a severely-dry year, but, on the contrary, a situation of wet years with heavy rainfall during monsoon months followed by severely-dry period calls for the need of rainwater harvesting during monsoon and its proper utilization during subsequent dry periods. Since SPI uses for the running sum of rainfall values at multi-time scales (1 to 24 months) and more variables depending on the statistical distributions used, it gives better assessment of meteorological drought at multi-time scales. The results shows that the SPI can be used for better assessment of drought as it considers larger range of moving sums of rainfall data. The positive SPI values show greater than medium precipitation, while negative SPI values indicate less than medium precipitation. The SPI, calculated for a desired period at any location, are based on the long term precipitation record (30 years or more). The SPI is a drought index based on the probability of an observed precipitation deficit occurring over a given prior time period. Standardized Precipitation Index (SPI) was calculated at different time scales (1, 3, 6, 12, and 24 months). Based on the drought analysis using the SPI criteria, appropriate crop planning and design of rainwater harvesting and storage structures in the drought affected areas can be proposed in drought affected areas. Drought is a decrease of water availability in a particular period and over a particular area. Droughts have been dramatically increased in number and intensity in many parts of the world. The main constraint in the performance of our proposed CBF model seems to be the number of past forecasts available for training the ANN.ĭrought is a natural and worldwide phenomenon, usually defined by periods of less than normal water availability, and is one of the major weather related hazards. At last, the performance of the model is calculated to validate the proposed CBF model and this shows that it is possible to use ANN as a machine learning technique in order to estimate future cloud burst forecast uncertainty from past forecasts data of bursting. By utilizing the concept of optimized ANN, the prediction accuracy is high in terms of percentage of correct predication and with minimum percentage of incorrect prediction. Basically, PSO separate the cloud burst recorded data using a novel fitness function that help to train the CBF model accuracy and if training is better, then the prediction accuracy will be high. Here the concept of Particle Swarm Optimization (PSO) is used as an optimization technique in pre-processing step to separate the previous year rainfalls data into two categories such minimum and maximum rainfalls. So, in this research we focus to develop a model using the optimized Artificial Neural Network (ANN) for prediction of cloud bursting in India and developed model in known as Cloud Burst Foresting (CBF) model for forecasting of rainfall or cloud burst based on the previous record of the bursting in different state. However, the atmosphere conditions are a highly chaotic system and always vary with time and weather forecasts are a major benefit for society and sustainable development. Over the last decades, weather forecasting models using the numerical data of various previous years have been improving steadily to provide a more predictable and accurate model.
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