## 1 Introduction  
A school of thought has gained traction among scholars recently that argues that many of the changes observed in water bodies in the recent past are at least partially attributable to human activities [1]. One of the most notable examples of such impacts is the change in inland water bodies, particularly saline lakes. A total of 44% of all lakes, volumetrically speaking, consist of large saline lakes [2]. However, many saline lakes worldwide are shrinking at alarming rates [3; 4]. In addition to endangering habitats and ecosystems, such phenomena could potentially lead to human health hazards as these lakes continue to deteriorate [5].  
In certain instances, human activities have been identified as the direct cause of such occurrences. Lakes Alemaya and Hora-Kilole, Ethiopia [6]; the Dead Sea, West Bank [7]; Lake Ebinur, China [8]; and Lake Corangamite, Australia [9] are merely a few examples of such cases. The Aral Sea and Owens Lake are two classic examples of the desiccation of salt lakes [5]. In other cases, climate change has been identified as the underlying cause of the issue [10]. Natural variation in hydro-climatic variables is another well-studied reason behind shrinking patterns in lakes [11; 12]. More often, however, a combination of the previously mentioned factors is the reason behind the deterioration of an inland water body [13; 14; 15; 16]. It is worth noting, however, that detection, let alone understanding the cause of such deterioration, is a challenging task [17].  
The computational challenges of identifying such changes notwithstanding, detecting and, in turn, understanding such depletion patterns in a timely fashion are quite crucial from the water resources management perspective. It is worth noting that often the uncertainty associated with these patterns is one of the main notable challenges to understanding and, in turn, unraveling these depletion patterns. That said, the idea is that, should unnatural causes be detected during the investigation, one can attempt to mitigate or halt these adverse impacts. On that note, implementing remote sensing (RS)-based data to monitor and unveil the depletion patterns of inland water bodies has proven to be an effective practice e.g., [18; 19; 20; 21; 22]. It should be noted, however, that while RS can help extract vital raw data, additional data processing schemes are required to detect such deterioration patterns. To that end, some scholars have used statistical frameworks to discern the causes of shrinking surface area e.g., [17; 23]. Although limited examples are available for this, machine-learning-oriented methods are another viable alternative to interpreting hydro-climatic data to reveal these changing patterns in inland water bodies e.g., [24]. Often, these studies rely solely on classification or clustering methods to unveil such patterns. An interesting angle is incorporating regression data mining methods such as artificial neural networks (ANN) to monitor these patterns from a more numeric-oriented perspective. The added benefit of such an approach would be that the results obtained from the data mining models would be complementary to the statistical-based analysis. On that note, this study utilizes both statistical and data mining-oriented analysis to evaluate hydro-climatic and RS-based data as a potential means to unveil hidden depletion patterns in inland water bodies. Lake Maharlou, Iran, is a case study to demonstrate this idea. Maharlou, once considered a permanent inland water body in southwestern Iran, may have reached a critical condition, and seasonal drying of the lake has become a recurring phenomenon [25], which makes it an ideal case to explore the potential behind this framework.