A simulation based approach to quantify the difference between event-based and routine water quality monitoring schemes

Acknowledgements The authors would like to thank Dr. Rob Mann and the Sydney Catchment Authority for providing the data and information used in this work.Peer reviewedPublisher PD

Desertification vulnerability index—an effective approach to assess desertification processes: A case study in Anantapur District, Andhra Pradesh, India

Not AvailableThere is a need for the up‐to‐date assessment of desertification/land degradation maps that are dynamic in nature at different scales for comprehensive planning and preparation of action plans. This paper aims to develop the desertification vulnerability index (DVI) and predict the different desertification processes operating in Anantapur District, India, based on machine language techniques. Climate, land use, soil, and socioeconomic parameters were used to prepare DVI by a multivariate index model. The computed DVI along with climate, terrain, and soil properties was used as explanatory variable to predict the desertification processes by using a random forest model. About 14.2% of the area was created as a training dataset in 9 places for modeling and remaining area was tested for prediction of desertification processes. We used desertification status map (DSM) of Anantapur District prepared under Desertification status mapping of India–2nd cycle as a reference dataset for calculation of accuracy indices. Kappa and classification accuracy index were calculated for training and validation datasets. We recorded overall accuracy rate and kappa index of 85.5% and 75.8% for training datasets and 71.0% and 51.8% for testing datasets. The results of variable importance analysis of random forest model showed that DVI was the most important predictor followed by potential evapotranspiration and Normalized Difference Vegetation Index for prediction of desertification processes. The results from this work given new insight into using the existing knowledge on prediction of desertification in unvisited areas and also quick update of DSM maps.Not Availabl

Predicting Soil pH by Using Nearest Fields

The 39th SGAI International Conference on Artificial Intelligence (AI 2019), Cambridge, United Kingdom, 17-19 December 2019In precision agriculture (PA), soil sampling and testing op-eration is prior to planting any new crop. It is an expensive operationsince there are many soil characteristics to take into account. This papergives an overview of soil characteristics and their relationships with cropyield and soil profiling. We propose an approach for predicting soil pHbased on nearest neighbour fields. It implements spatial radius queriesand various regression techniques in data mining. We use soil dataset containing about 4,000 fields profiles to evaluate them and analyse theirrobustness. A comparative study indicates that LR, SVR, andGBRTtechniques achieved high accuracy, with the R2 values of about 0.718 and MAEvalues of 0.29. The experimental results showed that the pro-posed approach is very promising and can contribute significantly to PA.Science Foundation IrelandInsight Research CentreOrigin Enterprise

Uncertainty and uncertainty propagation in soil mapping and modelling

In previous chapters, the use of geostatistical modelling for soil mapping was addressed. We learnt that one of the advantages of kriging is that it not only produces a map of predictions but that it also quantifies the uncertainty about the predictions, through the kriging standard deviation. In this chapter we will look into this in more detail. We will also examine another way to assess the accuracy of soil prediction maps, namely, through independent validation. This approach has the advantage that it is model-free and hence makes no assumptions about the structure of the spatial variation and relationships between the target soil property and covariates. Finally, we will examine how uncertainties in soil maps propagate through environmental models and spatial analyses. Throughout this chapter we will use the Allier data set and case study, Limagne rift valley, central France, to illustrate concepts and methods. We will only consider soil properties that are measured on a continuous-numerical scale. Many of the concepts presented can also be extended to categorical soil variables, but this is more complicated and beyond the scope of this chapter