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