G- ANFIS S-ANFIS
4.1 Performance of Base Models at Different Stations
4.1.1 Effect of Input Meteorological Variables
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RESULTS AND DISCUSSION
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using C1 to C63 can be examined by analysing Figure 4.1(a) to Figure 4.1(c) column-by-column. The MAE of the MLP’s estimations ranged from 0.0236 mm/day to 0.8457 mm/day whereas the RMSE ranged between 0.0289 mm/day and 1.0645 mm/day. Low MAE and RMSE values are found to be concentrated at the left-hand side of the heat maps, which correspond to the input combinations that consist of higher number of meteorological variables. The results are reasonable such that by providing more meteorological variables to the MLP, the model will be able to fetch more information from the inputs given.
That is to say, to develop a good machine learning model for ET0 estimation at different stations in Peninsular Malaysia, more meteorological variables have to be collected.
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(d)
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Figure 4.1: (a) MAE, (b) RMSE, (c) MAPE, (d) R2 and (e) MBE of MLP Estimation at Different Stations with Different Input Combinations
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In general, the error distribution in Figure 4.1(a) is similar to Figure 4.1(b) whereby Station 48600 (Pulau Langkawi), Station 48601 (Bayan Lepas), Station 48603 (Alor Setar) and Station 48615 (Kota Bharu) registered the highest MAE and RMSE when the number of input meteorological variables were lesser (C50 onwards). However, when the errors were rectified by calculating the MAPE, Station 48647 (Subang), Station 48649 (Muadzam Shah), Station 48650 (KLIA) and Station 48657 (Kuantan) had the highest MAPE among the 12 stations as shown in Figure 4.1(c) (C50 onwards as well). In other words, the MLP could estimate better in the northern regions than the stations located in the central Peninsular Malaysia.
In terms of generalisability, the MLP performed well in which the model could achieve R2 values of at least 0.60. Nevertheless, there are some exceptions observed. When trained using the input combinations C50, C59 and C62, the MLP had rather poor performance. This could be explained by the absence of key meteorological variables, which will be discussed later. From the aspect of the MBE, the MLP registered a maximum underestimation of -0.0126 mm/day and could overestimate up to 0.0159 mm/day. These values correspond to a bias of -0.33 % and 0.39 % at their respective stations, which can be considered insignificant. It is interesting to note that the occurrence of underestimation (blue) and overestimation (red) increased as the number of meteorological variables fed as inputs was reduced. On top of that, estimation bias consistently appeared in the MLP estimation of ET0 at Station 48620 (Sitiawan).
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columns periodically appear irrespective of the stations tested. For instance, dark bands (poor performance) appeared at C7, followed by C12, C16, C19, C21, C22 and so on. When referring Figure 3.3, the mutual characteristic of these input combinations is the absence of Rs as one of the input meteorological variables. In other words, Rs can be claimed as the key meteorological variable in the estimation of ET0 in Peninsular Malaysia by using the MLP. This also explains the low R2 value attained for C50, C59 and C62. In fact, C50 is the union set of C59 and C62, which consist of only u and Tmin, respectively. Further deduction can be made to imply that the u and Tmin are the two least important features for ET0 estimation in Peninsular Malaysia.
The finding of the key and least essential meteorological variables for ET0 estimation shall be supported by scientific theory as a step forward to reduce the opacity of the black-box operation of machine learning based estimation. Ndiaye, et al. (2017) analysed the sensitivity of the ET0 towards the change in meteorological variables in the region of Burkina Faso, which had a similar climate pattern to that of Peninsular Malaysia. The authors reported that the Rs was the most influential meteorological variable on the ET0. On top of that, RH would become important during dry seasons which corresponded to low vapour pressure. In Kenya, it was argued that the Rs alone represented multiple scenarios that could probably affect the ET0 (Odongo, et al., 2019).
The authors stated that low Rs could be due to the increase in the cloud coverage as well as the aerosols. In other words, that would be well associated with low
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surface temperature and higher RH. This situation had made Rs the key meteorological variables that dictate the regime of the ET0.
Besides looking at areas with similar climates to Peninsular Malaysia, Pour, et al. (2020) performed a thorough analysis to study the relationship of different meteorological variables in Peninsular Malaysia itself. The trend analysis showed that the Rs correlated well with the ET0 where both exhibited close to identical trends throughout the study period, whereas the RH had a reversed trend as compared to the ET0. Moreover, the results for sensitivity of ET0 towards the other meteorological variables were actually the opposite of that reported in Burkina Faso (Ndiaye, et al., 2017). Pour, et al. (2020) discovered that in Peninsular Malaysia, the Rs and the RH were least influential towards the ET0, which contradicted their findings in the trend analysis.
However, the authors did not provide any further explanation on this matter.
The results in this study point to the fact that Rs is the key or essential meteorological variable for estimating ET0 in Peninsular Malaysia using the MLP. This finding is in agreement with all the cited research works, except for the sensitivity analysis of Pour, et al. (2020). This discrepancy could be due to the high associative relationship between the time series data of ET0 and Rs in the study by Pour, et al. (2020). Furthermore, despite having similar seasonal trends, the fluctuation of Rs in the study is very low, as compared to the ET0. Therefore, the change in the trend of the ET0 would be mainly driven by the anomaly that occurred in other meteorological variables. The significant
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in the values of ET0 that could not be accurately estimated by the MLP fed with only Rs.
Besides matching many empirical sensitivity studies, the discovery of the Rs to be the key features for ET0 estimation also aligns well with the nature of the ET process. Essentially, incoming radiation from the Sun is the sole energy input to drive water depletion from the Earth’s surface (Cascone, et al., 2019). Moreover, the Rs in Peninsular Malaysia is less prone to seasonal variation due to the geographical characteristic close to the Equator (Pour, et al., 2020). In fact, the ET is also strongly affected by other environmental conditions such as temperature and humidity. Nevertheless, this study revealed that the MLP was able to perform good estimations of ET0 at various locations with only the Rs as input. This is because the other environmental conditions (temperature and humidity) depend on the Rs. In other words, the change Rs
value alone can actually be translated to the change in temperature or humidity.
High Rs would correspond to high temperature, which would in turn decrease the value of RH. The MLP can be improved by increasing the number of
“complementary” meteorological variables in the training data as an effort to provide more explanatory features on the environmental conditions.
Of all the six meteorological variables used in this research work, only the u is independent of the Rs. However, in Peninsular Malaysia, the average
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value of u is relatively low, and that in turn reduces its contribution towards the ET.
The study on the effect of input meteorological variables provides a clear picture that can enhance prioritisation during the data collection process. The Rs
should be collected as it is the key meteorological variable for ET0 estimation, followed by other complementary meteorological variables in Peninsular Malaysia. It is noteworthy that the complementary meteorological variables needed to further enhance the ET0 estimations were different across the whole study area. In other words, for different stations, the optimum input combinations could be different. Discussion on this finding will be presented in this thesis in Section 4.1.3.