The trained NN-I and NN-II have been used for the prediction of environmental degradation status and equitable quality of life levels consequent upon developmental interventions. The output of NN-I provides the environmental degradation status levels with respect to air, water, and land media; and problems relating to solid waste and noise;. while the output of NN-II gives equitable quality of life levels in terms of maximum and minimum quality of life levels in urban areas, and average quality of life in rural areas.
The maximum and minimum quality of life levels observed across urban areas of the tehsil, and average quality of life in rural areas have been taken together as indicator of equity of quality of life across the tehsil.
The results of consequence analysis, for select tehsils, constituting environmental status degradation indices and Quality of Life levels are given in Table 1 for present, Business as Usual, and Preferred scenarios for the year 2021 A.D. The normalised values shown in the Table 1 assign 0.7 to the region with highest intensity of hotspots in the present scenario.
Consequence analysis is an integral part of developmental planning based on the premises of carrying capacity. Neural network based modelling approach provides a means of identifying a system of complex and non linear relations amongst resource endowment - socio - economic activities - environmental media status - quality of life levels. The data requirement for training a neural network for regional system identification is less in comparison to integrative causal models, media and resource specific regression models, and optimization models
Table 1: Quality of Life Levels in Selected Tehsils of National Capital Region for Present (Pres-Sc),Business as Usual(BaU), and Preferred (Pref-Sc) Scenarios for the Year 2021 A.D.
Region
(1) |
Quality of Life |
|
Urban Maximum |
Urban Minimum |
Rural |
|
Pres-Sc
(2) |
BaU
(3) |
Pref-Sc
(4) |
Pres-Sc
(5) |
BaU
(6) |
Pref-Sc
(7) |
Pres-Sc
(8) |
BaU
(9) |
Pref-Sc
(10) |
|
Alwar |
0.74 |
0.63 |
0.70 |
0.47 |
0.15 |
0.90 |
0.57 |
0.42 |
0.92 |
|
Anupshahr |
0.48 |
0.83 |
0.95 |
0.23 |
0.08 |
0.94 |
0.45 |
0.08 |
0.91 |
|
Behror |
0.63 |
0.63 |
0.92 |
0.40 |
0.39 |
0.74 |
0.31 |
0.49 |
0.62 |
|
Faridabad |
0.67 |
0.47 |
0.88 |
0.32 |
0.05 |
0.59 |
0.46 |
0.22 |
0.75 |
|
Garhmuk-
Teshwar |
0.70 |
0.83 |
0.93 |
0.40 |
0.06 |
0.93 |
0.50 |
0.56 |
0.92 |
|
Ghaziabad |
0.69 |
0.50 |
0.94 |
0.36 |
0.04 |
0.74 |
0.40 |
0.21 |
0.77 |
|
Gurgaon |
0.84 |
0.37 |
0.84 |
0.13 |
0.42 |
0.64 |
0.40 |
0.14 |
0.56 |
|
Khurja |
0.67 |
0.48 |
0.92 |
0.34 |
0.63 |
0.92 |
0.49 |
0.05 |
0.86 |
|
Meerut |
0.87 |
0.89 |
0.91 |
0.29 |
0.00 |
0.85 |
0.56 |
0.05 |
0.78 |
|
NCT-Delhi |
0.83 |
0.94 |
0.80 |
0.21 |
0.00 |
0.71 |
0.58 |
0.28 |
0.90 |
|
Panipat |
0.88 |
0.90 |
0.96 |
0.26 |
0.00 |
0.87 |
0.38 |
0.07 |
0.77 |
|
Rohtak |
0.70 |
0.71 |
0.95 |
0.23 |
0.06 |
0.80 |
0.34 |
0.33 |
0.89 |
|
Sonipat |
0.70 |
0.70 |
0.95 |
0.20 |
0.06 |
0.78 |
0.50 |
0.47 |
0.85 |
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