Proceedings of the Symposium on Global and Environmental Monitoring: Proceedings of the Symposium on Global and Environmental Monitoring

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Persistent identifier:: 856665355

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts ; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856665355

Language:: English

Document type:: Multivolume work

Volume

Persistent identifier:: 856669164

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Scope:: XIV, 912 Seiten

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856669164

Illustration:: Illustrationen, Diagramme, Karten

Signature of the source:: ZS 312(28,7,1)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: International Society for Photogrammetry and Remote Sensing, Commission of Photographic and Remote Sensing Data

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [WP-1 ADVANCED COMPUTING FOR INTERPRETATION]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: BACK PROPAGATION NETWORK FOR IRRIGATION SUITABILITY CLASSIFICATION OF STRESSED LANDS: A CASE STUDY IN PAKISTAN. Gauhar Rehmann, Abdul Fatah Shaikh, M. A. Sanjrani

Document type:: Multivolume work

Structure type:: Chapter

Dummy case representations were also inserted prior to network initialization in order to assess degra dation of final convergence and/or the measure of freak case over-ride available. All the cases con verged on or below threshold setting for weighted convergence. The learned model was progressively tested, first for interactions within the subgroups and then against random selections from specified case characteristics. The subgroup convergence results were converted to percentages and then ascertained across classification categorizations SI through N2. Subgroups accounting foi <40 % score against their respective classifications were noted for their departure tendencies to other classifica tions. The results of subgroup interactions and random selections were tabulated and graphed for presentation as figs. 1 through 7. The model, clas sifications were transferred to the polygon deline ations within 1:20.000 scale sample area maps to represent irrigation suitability assessments (figs. 8 & 9). Suffixes for classification symbology were not used because of incompatibility with I lie 1:20,000 wink ing scale. However, it would be possible to int in duce such elemental descriptions within the neuial network against more refined field da.ta that, would lead to net incremental irrigation benefit studies under a project development plan. RESULTS Within-group interactions reveal mixed results. Some interactions were very stiongly borne by mini mal of descriptions, e.g. in Si category, leaving aside < 24 % within-case accountability for water quality, the soil characteristics and ionic con centrations rated at 60-75 % consistency , and in many cases > 70 % definition accuracy with only one parameter description. Water quality definitions for this category are heavily influenced by the like descriptions within S2 category. This is also true to some extent for S3 category. The slow take off of the curve in fig. 3 explains this ten-deucy and is contrasted well against the otherwise excel lent showing of >80% in water quality in tig. 4. The strong showing by the remaining subgroups in SJ category is primarily' contributed by an explicit set of descriptions for the best quality id land with no fuzzy overlap with the degrading .land char acteristics within the remaining categorizations. The less than optimum segregations for water quali ty 7 parameters are partly influenced by the diffi culty iu adapting the recommendations of University of California CommitLee of Consultants. 1974, im SAR vs ECw interactions to the oites more n innionJy known ppm descriptions forwarded Lr.v the United States Department: of Agriculture. Figures 4 through 7 dwell on the heuristics of land evaluation with subgroup borrowings ot parameter descriptions, for every slop of the convergence model.. The central tendency is mound the maigin- nally suitable land when* tin*, "good" characteris tics arc balaiicing-oi f the less-l han-dcs i i nL 11 characteristics. Category S2 had the most, number of training models prepared lor network learning, and the tesull in fig. 4 illustrates tire sharp convergence and con sistency of the scores. "S3" curve is compaiat ive.ly slow but sut e in approach ing convergence. Iho point of inflection iu t i g. 6 is because of the bias dep ress jug. i he .1 i valent cations resulting in their pre- vjpit.al inn by the high IICf)3 presence. This loss in potency of cationic behavior is actually a sore thumb like indication to the generated model to progress towards a sharper convergence. Unlike the regression er.t imat ion, where the emphasis is on the least squares fit , t he neural network model explores maximum divergency and vat iation, and wayward beha vior is most conducive to problem identification. However, this waywardness is desirable only within the subgioupings established for the model. Figure. 7 is a hallmark of drastic swings in the model led appropriations. The rise is sharp because of favorable endorsements from water quality, how ever I lie near convergence loses momentum arid starts l.o collapse because of ionic concentration borrow ings from S3 and N1 to portray saline-alkali bo- ha v ioi . A fantastic ol-servat ion was made concerning the use of .lummy case doscri.pl ions; these cases had been in serted to observe the error propagation in the neu ral network, in one of the cases, the water quality for S2 category had been changed (piioi to network learning) to the one appropriate for S3. The within subgroup analysis repeatedly failed to give any cre dit to the S2 category, indicating the over-ride in the model for inojdentaJ errors in input. While seveial characteristics were used towards sam ple case definitions, only 12 selections were avail ed (three from each subgroup) for studying the cumu lative rate of convergence the ultimate test of the model, for each suitability categorizations. While the line of convergence is not bordering on exactitude, it may be considered, in the ’neural sense' of the way, a best: fit. This is because the model oversees the number of times a case charac teristics had been used during the formula!ion of samples. If a certain paiamefer, e.g. response to organic matter amendment, has been highlighted re peatedly. then its weighted influence will propor tionately increase. It s omission from model test ing would indicate depressed into of convergence, and likewise its inclusion may be the next ihing to full convergence. Since each subgroup contributed only 3 characteristics, there were 1 i.kely to be omissions that influenced the .sl.oj.re of the line of conver gence. However, for. (lemonsl:rat ion purposes, the sLope of the line has been averager! over several characteristic combinations for each subgroup. (,'ONUI US IONS This study attempted an adaptation of software emu- la led neuial net wo l k mod« 1 to the prescribes of irrigated land suitability ass.* -.smeii t . Though the model was not: tweaked to its optimum because of coarser pre-set orroi eiiteiin. lack of oxperimenta- I ion with in( ci node selections. fewer number of sam ple cases, tin* results have been promising. In all fairness, an evaluator who is already prone to his intuit ton is mostly interested in “stabl i siting zones of significance lot match-up of land cover and land use dim aet.pr i si Ls. I'o he .»hie to accomplish the a!rove decision-making in an automated sense of the way with resilience ami consistency would b«_> a pla cid achievement . Foi Hie case study. the neural neluoik lias not only facilitated subjective classi fication of data lull .•»1 >i» sei veil a--, t he cornerstone tor [ in ju.se|a 1 advice lending to land rehabilitation .1! t ivit ¡is. all governed by the equity cons idera- t ions. In a country, wiu'ii- lert ¡ary level of infor mation is s* j hi. mi or da i tied iu map-making, the element ■ ! suggest ibilit.y for 'reasoned' ut il.izatjon of i r- i ¡gated land is a signll it. ant development. 315

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