Data-driven low-complexity nitrate loss model utilizing sensor information — Towards collaborative farm management with wireless sensor networks

Excessive or poorly timed application of irrigation and fertilizers, coupled with the inherent inefficiency of nutrient uptake by crops result in nutrient fluxes into the water system. The ability to predict nutrient-rich discharges, in real time, can be very valuable to enable reuse mechanisms within farm systems. Wireless Sensor Networks (WSNs) offer an opportunity to monitor environmental systems with unprecedented temporal and spatial resolution. As part of our previous work, we proposed a novel framework (WQMCM) to combine increasingly common local farm-scale sensor networks across a catchment to learn and predict (using predictive models) the impact of catchment events on their downstream environments, allowing dynamic decision. Existing models use complex parameters which are difficult to extract and this, coupled with constraints on network nodes (battery life, computing power etc., availability of sensors) makes it necessary to develop simplified models for deployment within the networks. The paper investigates data-driven model for predicting daily total oxidized nitrate (TON) fluxes by seeking simplification in model parameters and using only a yearlong training data set. Data from a catchment in Ireland is used for training the model. Model simplification is investigated by abstracting details from an existing nitrate loss model. By using M5 decision tree model on the training samples of the proposed parameters, results give R2 as 0.92 and RRMSE as 0.26. The proposed novel model gives better results with fewer samples and simple parameters when compared to the traditional model. This shows promise for enabling real time nutrient control and management within the collaborative networked farm system.

[1]  Peter P. Motavalli,et al.  Nitrogen losses in runoff from three adjacent agricultural watersheds with claypan soils , 2006 .

[2]  Nathalie Villa-Vialaneix,et al.  A comparison of eight metamodeling techniques for the simulation of N2O fluxes and N leaching from corn crops , 2012, Environ. Model. Softw..

[3]  Nick Harris,et al.  Empirical modeling and simulation for discharge dynamics enabling catchment-scale water quality management , 2014 .

[4]  Rodger B. Grayson,et al.  Before and after riparian management: sediment and nutrient exports from a small agricultural catchment, Western Australia , 2003 .

[5]  J. Arnold,et al.  HYDROLOGICAL MODELING OF THE IROQUOIS RIVER WATERSHED USING HSPF AND SWAT 1 , 2005 .

[6]  Fusuo Zhang,et al.  Nitrogen dynamics and budgets in a winter wheat-maize cropping system in the North China Plain , 2003 .

[7]  D. Helsel,et al.  NUTRIENTS IN THE NATION'S WATERS : TOO MUCH OF A GOOD THING ? , 1996 .

[8]  R. Abrahart,et al.  Detection of conceptual model rainfall—runoff processes inside an artificial neural network , 2003 .

[9]  Lars Bergström,et al.  Simulated nitrogen dynamics and losses in a layered agricultural soil , 1987 .

[10]  Giovanni Coco,et al.  The use of machine learning algorithms to design a generalized simplified denitrification model , 2010 .

[11]  Nick Harris,et al.  Review: The impact of agricultural activities on water quality: A case for collaborative catchment-scale management using integrated wireless sensor networks , 2013 .

[12]  M. Vanclooster,et al.  Metamodelling: Theory, concepts and application to nitrate leaching modelling , 2006 .

[13]  Nick Harris,et al.  Water quality monitoring, control and management (WQMCM) framework using collaborative wireless sensor networks , 2014 .

[14]  Shuguang Liu,et al.  Simulation of nitrous oxide and nitric oxide emissions from tropical primary forests in the Costa Rican Atlantic Zone , 2000, Environ. Model. Softw..

[15]  P. Ball,et al.  Nitrogen relationships in intensively managed temperate grasslands , 1984, Plant and Soil.

[16]  François Anctil,et al.  Comparison of Machine Learning Regression Methods to Simulate NO 3 Flux in Soil Solution under Potato Crops , 2014 .

[17]  Bernard Vanlauwe,et al.  Combined application of organic matter and fertilizer , 2001 .

[18]  Arlen W. Harbaugh,et al.  MODFLOW-2000, The U.S. Geological Survey Modular Ground-Water Model - User Guide to Modularization Concepts and the Ground-Water Flow Process , 2000 .

[19]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[20]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[21]  J. Palous,et al.  Machine Learning and Data Mining , 2002 .

[22]  Changsheng Li,et al.  A model of nitrous oxide evolution from soil driven by rainfall events: 1. Model structure and sensitivity , 1992 .

[23]  Changsheng Li,et al.  An integrated model of soil, hydrology, and vegetation for carbon dynamics in wetland ecosystems , 2002 .

[24]  Marcello Restelli,et al.  Tree‐based reinforcement learning for optimal water reservoir operation , 2010 .

[25]  Richard H. Hawkins,et al.  The NRCS Curve Number , a New Look at an Old Tool , 2001 .

[26]  Avi Ostfeld,et al.  Data-driven modelling: some past experiences and new approaches , 2008 .

[27]  J. C. Ryden,et al.  Nitrogen relationships in intensively managed temperate grasslands , 1984 .

[28]  Andrea Castelletti,et al.  Assessing the predictive capability of randomized tree-based ensembles in streamflow modelling , 2013 .

[29]  D. D Adelman.  Simulation of irrigation reuse system nitrate losses and potential corn yield reductions , 2000 .

[30]  D. Solomatine,et al.  Model trees as an alternative to neural networks in rainfall—runoff modelling , 2003 .

[31]  J. R. Quinlan.  Learning With Continuous Classes , 1992 .

[32]  Alex J. Cannon,et al.  Daily streamflow forecasting by machine learning methods with weather and climate inputs , 2012 .

[33]  William Salas,et al.  Modeling nitrate leaching with a biogeochemical model modified based on observations in a row-crop field in Iowa , 2006 .

[34]  W. Reed Green,et al.  Modeling phosphorus in the environment , 2008 .

[35]  V. Singh,et al.  Computer Models of Watershed Hydrology , 1995 .