## 4 Results and Discussion  
The monthly fluctuations in Maharlou Lake's surface over the period from 1992 to 2017 are depicted in Figure 4. While the lake's surface area shrank from May to October (i.e., as highlighted by lower average values in these months), the interquartile range (IQR) for these particular months is higher, which indicates that the lake's area exhibits more significant fluctuation during these months. The annual average of the lake's surface area was graphed from 1992 to 2017 (Figure 5). The Pettit test has identified a significant shift in the time series, which occurred in 2007. Statistically speaking, the said test indicates that the lake's surface area experienced such an abrupt change that the data series can be divided into pre-change-point and post-change-point sets. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Model I** & **Model II** \\ \hline Input & Rainfall, temperature, streamflow, lake’s surface area from last time step & Last 24 steps of lake’s surface area data \\ layers & 2 & 2 \\ No. of neurons of 1 st layer & 25 & 20 \\ No. of neurons of 2 nd layer & 10 & 15 \\ Output & One step of lake’s surface area data & 12 steps of lake’s surface area data \\ Activation function between hidden layers & Relu & Relu \\ Activation function between hidden layers & Linear & Linear \\ and output layers & 0.01 & 0.01 \\ Learning rate & Adamax & Adamax \\ Optimizer & Mean squared error & Mean squared error \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hyperparameters settings of the proposed ANN models. Figure 4: Average monthly variation of Maharlou Lake’s surface area from 1992 to 2017. Figure 5: Annual average of Maharlou Lake’s surface area. Each month's time series was isolated to investigate this alleged abrupt change further. As such, each was represented by a series that showed the function of the lake's surface area in the said month from 1992 to 2017. Similarly, the Pettit test was employed to identify any potential jumping points in these series. The monthly changing points for each month were determined (Table 2). Apart from November and December, for which jumping points could be identified in 2001 and 2006, respectively, the rest of the months experienced breaking points in 2007. As stated earlier, one can also resort to data mining methods to provide additional context about the hidden structures in the dataset. On that note, an MLP model (model I) was trained to simulate the lake's surface area time series using extraneous factors. Figure 6 visualizes the overall performance of the calibrated model I. Figure 6a compares the obtained results from the calibrated model I against the observed data, and Figure 6b shows the residual values that represent the absolute prediction error at each timestep. Based on the obtained results, model I's performance can be broken down into two sections, where the first portion represents the model's results before 2008 and the second section denotes the 2008-onward data. As can be seen in Figure 6, the accuracy of model I in the first portion of the dataset can be deemed acceptable. However, the prediction accuracy of the regression model drastically declines in the second portion of the simulation. This, in and of itself, hints that the underlying structure of the data has experienced a sudden change, to the point that the calibrated mode cannot accurately capture the behavior of the second portion of the time series. The next stage of the investigation is the identification of any potential gradual changes in the structure of the dataset using statistical analysis. The Mann-Kendall test and Sen's slope estimator were used to identify the gradual changes in surface area (Table 3). The results show a statistically significant negative trend component in each of the lake's surface area time series. These components are more pronounced from June to October; moderate in May, November, and December; and mild from January to April.