The effect of optimism bias and governmental action on siltation management within Japanese reservoirs surveyed via artificial neural network. Issue 1 (2nd January 2020) - Landwehr, Tobias; Kantoush, Sameh Ahmed; Pahl-Wostl, Claudia; Sumi, Tetsuya; Irie, Mitsuteru
ABSTRACT: Reservoirs are installed as long-term assets to guarantee water and energy security for decades, if not centuries. However, the effect of siltation undermines reservoirs' sustainability because it significantly reduces the reservoirs' original capacity. The present paper attempts to evaluate the global reservoir siltation problem with the optimism bias theorem introduced by Kahneman and Tversky and applied to infrastructural mega-projects by Flyvbjerg and Ansar using artificial neural networks (ANNs) algorithms for large Japanese reservoirs. Japan possesses suitable long-term data and a legal directive concerning the sediment capacity siltation duration, which serves as a valid guide to check whether, over the past 100 years, engineers, planners and managers were capable of judging the sediment input correctly. Various ANN models were established to emulate Japanese reservoir siltation behavior. The networks demonstrate that reservoirs in Japan suffer from optimism bias. In contrast to the law, the dead storage volume of an average dam is supposed to reach capacity after 52 years. This finding joins the overall observation that mega-projects generally and globally suffer from optimism bias. The emulations were subsequently screened for a presumed influence of governance actions, namely, indicating plus monitoring and the change in the market competition situation. While reservoir siltation appears to continue regardless of the level of competition in public procurement, monitoring directives appear to have a considerable impact on improved siltation management, which demonstrates that dedicated governance action can significantly strengthen the sustainable behavior of key infrastructure elements such as reservoirs.