Abstract
Abstract. This paper introduces research done on the automatic preparation of remediation plans and navigation data for the precise guidance of heavy machinery in clean-up work after an industrial disaster. The input test data consists of a pollution extent shapefile derived from the processing of hyperspectral aerial survey data from the Kolontár red mud disaster. Three algorithms were developed and the respective scripts were written in Python. The first model aims at drawing a parcel clean-up plan. The model tests four different parcel orientations (0, 90, 45 and 135 degree) and keeps the plan where clean-up parcels are less numerous considering it is an optimal spatial configuration. The second model drifts the clean-up parcel of a work plan both vertically and horizontally following a grid pattern with sampling distance of a fifth of a parcel width and keep the most optimal drifted version; here also with the belief to reduce the final number of parcel features. The last model aims at drawing a navigation line in the middle of each clean-up parcel. The models work efficiently and achieve automatic optimized plan generation (parcels and navigation lines). Applying the first model we demonstrated that depending on the size and geometry of the features of the contaminated area layer, the number of clean-up parcels generated by the model varies in a range of 4% to 38% from plan to plan. Such a significant variation with the resulting feature numbers shows that the optimal orientation identification can result in saving work, time and money in remediation. The various tests demonstrated that the model gains efficiency when 1/ the individual features of contaminated area present a significant orientation with their geometry (features are long), 2/ the size of pollution extent features becomes closer to the size of the parcels (scale effect). The second model shows only 1% difference with the variation of feature number; so this last is less interesting for planning optimization applications. Last model rather simply fulfils the task it was designed for by drawing navigation lines.
Highlights
On October 4th, 2010 Hungary faced the worst environmental disaster in its history when the embankment of a toxic waste reservoir failed and released a mixture of 600,000 to 700,000 m3 of red mud and water
Readers should notice that this exploratory work is relevant for ex-situ remediation on extended areas where industrial disaster took place
Clean-up parcel feature class should be recreated based on the new target area (this is done in order not to have an empty area when the features will be shifted)
Summary
On October 4th, 2010 Hungary faced the worst environmental disaster in its history when the embankment of a toxic waste reservoir failed and released a mixture of 600,000 to 700,000 m3 of red mud and water. As positioning technologies are routinely employed in civil engineering for the guidance of heavy equipment for precise and efficient work it seems the shortcomings in the case of ex-situ remediation lays in the capacity to generate adequate remediation plans and in the lack of adapted GIS tools, models, methods and practice [1]. Readers should notice that this exploratory work is relevant for ex-situ remediation (remediation where excavation is done) on extended areas where industrial disaster took place (red mud, nuclear, chemical, etc.). In such cases heavy machinery is used and it makes sense to try to plan their moves precisely in order to save effort, time and money, in a similar way as precision agriculture or civil engineering do. Based on the results of the tests diverse proposals are formulated for the development of the final version of the models
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