Developing a Machine Learning Algorithm to Identify Hot Moments Using Mass Balance Approach at Watershed Scale
Authors: Thaasan Thakshajini , Quang Phung , Aloysius Noel 
Published: March 2021

&lt;p&gt;Preserving and promoting the sustainable use of natural resources while stabilizing healthy ecosystems under rapid environmental changes is a tremendous challenge for the international community. Science-based strategies are imperative to maintain and improve Earth&amp;#8217;s ecosystem. Our research is designed to improve predictive ability of managed ecosystems&amp;#8217; responses to changing weather patterns and human management. Specifically, our research seeks to develop conservation plans to improve water quality in streams and lakes, while maintaining the economic sustainability of food production systems. Reducing pollution loading into aquatic systems help improve the water quality and enhance ecosystem sustainability. Non-point pollution sources are predominant factors in increasing pollution into the water bodies. Identifying the pollution sources is important to mitigate the impact. For this reason, the main objective of our study is to identify the &amp;#8220;hot spots&amp;#8221; and &amp;#8220;hot moments&amp;#8221; of excessive nitrogen and phosphorus leaching from managed landscapes in the midwestern United States.&lt;/p&gt;&lt;p&gt;We developed a simple lumped model with three parameters to simulate key water fluxes - surface and subsurface runoff, and evapotranspiration (ET) in the Maumee River Basin. We designed a machine learning algorithm to identify &amp;#8220;hot moments&amp;#8221; using nitrogen mass balance approach at watershed-scale. The simple model helps to link the relationship between applied fertilizer and retained nutrients in the soil that the heterogeneous landscape and land management influence. Nitrogen retained in the soil will be used as an output variable and connected with predictor variable ET. Relationships between crop yield and water use in crop growth (ET) could be interpreted in a simple empirical formulation where relative change in crop yield is related to the corresponding relative change in ET, which can be expressed as,&lt;/p&gt;&lt;p&gt;1&amp;#8722;&amp;#119884;&lt;sub&gt;&amp;#119886;&lt;/sub&gt;/ &amp;#119884;&lt;sub&gt;&amp;#119909;&lt;/sub&gt;=&amp;#119870;&lt;sub&gt;&amp;#119910;&lt;/sub&gt; (1&amp;#8722; &amp;#119864;&amp;#119879;&lt;sub&gt;&amp;#119909;&lt;/sub&gt;/&amp;#119864;&amp;#119879;&lt;sub&gt;&amp;#119886;&lt;/sub&gt;)&lt;/p&gt;&lt;p&gt;where Yx and Ya are the maximum and actual yields, ETx and ETa are the maximum and actual evapotranspiration, and Ky is a yield response factor representing the effect of relative change in ET on crop yield. The developed algorithm will be trained, tested, and validated using the coupled water flux and crop yield models. We will then demonstrate how these relationships can be extended to complex watershed model simulations that account for key land management decisions, land use pattern, crop type, soil, and topographic variability. Ultimately, we hope our findings will enhance the knowledge related to the environmental policy and decision making.&lt;/p&gt;