## 4 Experiments and Analysis
### Experiment Results and Error Analysis  
After four experiments completed, we calculated their errors and hitting rates, shown in Table 5. We also drew comparison charts between predicted values and observed values (panels (a) in Figures 3-6), and time sequence diagrams of prediction errors (panels (b) in Figures 3-6). Figure 4: Testing results of the Case-based FCTF Reasoning System in experiment number two. (**a**) Comparison between Observation and Prediction; (**b**) Prediction error. Figure 3: Testing results of the Case-based FCTF Reasoning System in experiment number one. (**a**) Comparison between Observation and Prediction; (**b**) Prediction error. Firstly, the case-based FCTF reasoning systems with only one input condition and with two input conditions are used to predict the precipitation levels for weather forecasting, but lead to the non-accuracy of the prediction outputs. Table 5 shows that hitting rates in experiments number one and number two are both equal to zero, which means that the rates of prediction accuracy are zero. Figures 3a and 4a show that the case-based FCTF reasoning systems with only one input condition and with two input conditions never successfully forecasted any rainfalls during the experimental time period. However, they had forecast no rainfalls in all the days during the whole experiment, which makes the errors are always equal to or less than zero as shown in Figures 3b and 4b. Therefore, we believe that the case-based FCTF reasoning systems with only one input condition and with two input conditions are completely impossible to forecast precipitation. These results can also be explained on meteorology. Meteorologists point out that the conditions of rainfall formation are complex [35, 36], and using only one or two kinds of input conditions cannot forecast precipitation. Figure 5: Testing results of the Case-based FCTF Reasoning System in experiment number three. (**a**) Comparison between Observation and Prediction; (**b**) Prediction error. Figure 6: Testing results of the case-based FCTF reasoning system in experiment number four observed rainfall days in calculating average \(|Error|\). (**a**) Comparison between Observation and Prediction; (**b**) Prediction error. The case-based FCTF reasoning system with three input conditions improved heavily compared to experiments one and two, just after adding the relative humidity as the new input condition. Figure 5 shows that it can forecast some of the rainfall events. Hit rate, as shown in Table 5, which increased to 0.27, is much better than those in experiments one and two, which are both equal to zero. Average \(|error|\) plummeted from 1.52 levels to 1.25 levels, decreasing by 17.8%. This result preliminary notes that when the case-based FCTF reasoning system forecasts a complex system (such as weather system), adding a new input condition is one of the important and useful methods to increase the accuracy. This is also evident in the meteorology area. Compared with the atmospheric pressure and temperature, relative humidity has greater effect on precipitation [37; 38; 39]. However, this forecast result is still far from our expectations. We continued with the case-based FCTF reasoning system with four input conditions, which is the improvement of our system with three input conditions. At this time, we added wind speed as a new input condition. Table 5 shows that after average \(|error|\) dropped from 1.25 levels to 1.16 levels, there are plenty of rises in hit rate, from 0.27 to 0.36. Compared with other three experiments, the case-based FCTF reasoning system with four input conditions is the best of these four experiments. Such improvement is also very easy to explain using meteorological knowledge. That is, a larger surface wind can bring a steady stream of moisture from other place, which makes a rain sustainable and precipitation increasing [40].