Abstract
Analysis of urban climate changing is the basis for the implementation of storm water management measurements. Climate tensions such as changing precipitation patterns, fluctuations in temperature, and extreme events are already affecting water resources. For instance, precipitation pattern will be changed due to more water vapor in the atmosphere. Hence, it will not be evenly distributed. Some places will see more rain, others will get less snow. However, climate changes, such as the amount, timing, and intensity of rain events, in combination with land development, can significantly affect the amount of storm water runoff that needs to be managed. Firstly, this essay will be discussed about the prediction of climate change using a fuzzy neural network (FNN) and it shows the accuracy of this method for anticipating storm water. Secondly, based on the results of the first phase, it determines the critical area for preparing storm water systems with the application of GIS tools and technology.
Highlights
Climate change represents the principal challenge that humans will have to face in this century
It may be used to apply to water that originates with snow melt or runoff water from overwatering that enters the storm water system
Storm water that does not soak into the ground becomes surface runoff, which either flows directly into surface waterways or is channeled into storm sewers, which eventually discharge to surface waters such as river
Summary
Simulation of this approach has been carried out in MATLAB. The results were monitored for the purpose of assessing the performance of the proposed FNN based on LoLiMoT model. The number of actual data that applied in algorithm was 120 as input data for train and test phase. The graphs showed the results of prediction in 6 steps forward. It is clearly found that, the calculated and desired curves overlap for most of data values which shows that desired output and target were nearly similar. All outputs are in error range less than 1% at some stage in the training phase. Different parameters have been used to the performance of the designed network: RMSE (Root Mean Square Error), used to compute the performance of the simulation, is valued between real and simulated data.
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