Daily Water Level Prediction of Zrebar Lake (Iran): A Comparison between M5P, Random Forest, Random Tree and Reduced Error Pruning Trees Algorithms

Zrebar Lake is one of the largest freshwater lakes in Iran and it plays an important role in the ecosystem of the environment, while its desiccation has a negative impact on the surrounded ecosystem. Despite this, this lake provides an interesting recreation setting in terms of ecotourism. The prediction and forecasting of the water level of the lake through simple but practical methods can provide a reliable tool for future lake water resource management. In the present study, we predict the daily water level of Zrebar Lake in Iran through well-known decision tree-based algorithms, including the M5 pruned (M5P), random forest (RF), random tree (RT) and reduced error pruning tree (REPT). We used five different water input combinations to find the most effective one. For our modeling, we chose 70% of the dataset for training (from 2011 to 2015) and 30% for model evaluation (from 2015 to 2017). We evaluated the models’ performances using different quantitative (root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), percent bias (PBIAS) and ratio of the root mean square error to the standard deviation of measured data (RSR)) and visual frameworks (Taylor diagram and box plot). Our results showed that water level with a one-day lag time had the highest effect on the result and, by increasing the lag time, its effect on the result was decreased. This result indicated that all the developed models had a good prediction capability, but the M5P model outperformed the others, followed by RF and RT equally and then REPT. Our results showed that these algorithms can predict water level accurately only with a one-day lag time in water level as an input and they are cost-effective tools for future predictions.

Understanding children’s perceptions and activities in urban public spaces: The case study of Zrêbar Lake Waterfront in Kurdistan

As public spaces are often designed based on adults’ behavioural patterns and perceptions, children’s perceptions and physical needs based on their body size have received less attention in both the design of urban spaces and urban studies. Focusing on the interpretive reproduction theory, this study aims to investigate children’s perceptions of urban spaces. Using behaviour and mental mapping, this paper examines children’s activities in public spaces as well as their mental images of such spaces. Behaviour mapping was conducted over 37 days on the Zrêbar Lake Waterfront in Kurdistan. Sketches drawn by 36 children were then analysed to identify children’s perceptions of the Waterfront. Unlike conventional methods in which children’s demands are determined through caregivers, this study focuses on engagement with children. Results indicate that two main factors of actualised environmental affordances (AEA) and safety-conscious parenting practices (SPP) have a significant influence on children’s freedom of movement (CFM) and consequently children’s activities in public spaces.