Traditional practice of rural farmers is choosing the crop/crops and suitable fertilizers as per their local discussions and experience. To improve this, proposed method suggests as fallowing:
i. Farmer must get their soil repot from nearby KVK (once in two years).
ii. Depending on existing scenario, identify crop specific suitability and choose the crop/crops accordingly.
iii. Add appropriate fertilizers in adequate proportion as suggested.
This paper has more focus on step (ii) Computing crop specific suitability.
Implementation details
It is a software module. Real time dataset is collected from Agriculture University, Pune since year 2009-10 till 2013-14. Accurate training data set is used. Database stored using SQL and algorithm implemented using JAVA language. Some part of the following algorithm is already published
(Bhimanpallewar et al., 2017).
Algorithm: A hybrid machine learning algorithm
Probability distribution of the parameter
P (input/output parameter).
P = (P1, P2, …, Pn)
Most of the logic is based on decision tree algorithm which allows us to generate multiple outcomes (
Malik and Tarique, 2014)
(Wu et al., 2008) (
Flach, 2012). Here we are generating five outcomes
i.e. S1, S2, S3, N1 and N2.
Steps for algorithm
I. Divide the dataset into two parts training and testing dataset. Here, output vector is nothing but suitability levels for the cropland,
T = (T1, T2, T3, T4,T5)
i.e. Suitability levels S = {S1, S2, S3, N1,N2}
1. X- set of every parameter considered as input
i.e. (X
1, X
2,..., X
n)
Where; n = number of input parameters, T = output/class parameter.
2. Select one parameter output (T) or input (Xi) consider it as
p.
Calculate the probability distribution of that parameter.
a. Parameter may take categorical values [Ex. parameter Soil Topography which takes value {1 (Plain), 2 (Gentle slope), 3 (Deep slop)}].
b. Parameter takes continuous values, then split it depending on the probability of the range of parameter values occurred under the class value T
i, parameter values will be partitioned into categories using ranges [Ex. In Fig 1 probability distribution of parameter rainfall is as,
p1 (rainfall <= 38 centimeters),
p2 (rainfall > 38 centimeters)].
p = (p1, p2, p3, …, pn)
3. Calculate the Entropy of P using Equation.
![](data:image/png;base64,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)
..........(1)
[More the uniformity in the probability distribution, gives more information.]
4. T- set of records which are partitioned based on class values C
1, C
2, ..., C
k
when,
p- is the probability distribution of the partition (C
1, C
2, ..., C
k) then the information needed to identify the class of an element of T is as in Equation.
Info (T) = I (p)
p = (|C1|/|T|,|C2|/|T|, …, |CK|/|T|)..........(2)
5. After partitioning based on class value into sets T
1, T
2, ...,T
n
The information needed to identify the class of an element of T = the weighted average of the information needed to identify the class of an element of Ti,
i.e. the weighted average of info (Ti), Equation.
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAANIAAAA5CAYAAABDGnUDAAAgAElEQVR4Ae29B5hV1b0+fBiKWGOMGmssKddr1MRYEvUaNVFjFKXY0MTEaAI2ytDLwBQYQNTY6FWKgjUaE1NssaLYBZTehzIw9bR9djnv97zvb+0zBzRXxTzfvfcfzjx79jm7rL3Wr7e1dgK7P7sMgTyAnJ8H9/oX5W2vFn3kESAEtOV1EU+kbOPvPJDL2/lc1rd7XRPZwEeAAB7SyISNyCODwEsCPoAAyGZz1nY+Qr6l8V0ey//VG/OIwA2f2GxEBLOX43kgCkLAzwKRB3gNQJhDQFjmBVJksjnXTIR8FCDMBwK24B6kgMjHtoyPZofT0CfsIfgn/q8C8H9Lvx0/FBAZBsZcYZQFqX4HRhIzZQBkxDT86TtGIlOlG1PIR8ZPfj5CiEAMlEcKEbIIgpxuJSPx3qZMM8LdjPSZjET55vuhmGPK+HtRMbgUiJLI55LwyGgA0jliKkDQXCcGI16IQQo05LNAejPmTbkPQ0bfjdrQGE9t5vO7GelfwoxiDrbkIQ8fxAdRQlCHQUYEz9+6TP/IRESd00RRHp6fQz40DuIlaS8iryAXZBDkauF7W9kyPArWUIJR7eXynqTmv7dGcrAVRPkv1k52gPDkFkWmXebOmoSxIwcBwXbMnDkRidb7IdH+ICQSCexTksDBbRPYp3UC/SvvQj1xoWZSQLAGT0wbg95lY3U86fBM2HPbrZEKCNiFL8QQARrkkIcnDUKmyeXsmNlhhkheKiSTifI0+0yqyTTJhwi9nC7MeqEYsXA9GoCoDiEipAIgoKT0gVQmLcYlgexmpGLc7chINPw8mnMIkM02YPYD4zCqojeC1Ho8NH86fvaLbmKMvv164vaym4HkUnS+6Az0GTEemwHUyipsBjIf4G8zR6F/xThsAdAQAl7el2G5m5GK4b8r3x0j0R7LwEMyzDrxB5kRYiQyTZ5sQMnl1ElE/4f6i/6VDz/IiMeCXB5BFIrJGus8U1mZGgDbxYTJLNAYmpdF+RqFWUna3YxUjLyYkWxPQRXmKejITBnMnT0eI8tvE0zHT7wT98/7IxoB9Ol/C6qH3gCEazBv6l24bfg4rAPEZEASSL+NpyYMRt/y8SBG6CflEO1mpGLQ7/L3PBCl0uQaZOXFmNIxlZIrmHvm7RgzgQZ7ZC4yQxEyB/2UOUs0zIkc0gBVWxbYtuItHHVAAq+/9go6Xv1b/KTLrailZlKgI7WbkWKzuYDEHRkpiCzoQ9Pb8xsw/v5RGD70FjHSQ/On4h8fLENdHug3pCcqh/0W8NfirZf+jH4jx2GTY6SMvw3wF+OpCUMxaPQMMRLvoTFvAnK3aVcA/y59Ed1HCHwPzQgl85DzXWQoCYRN4gcSPfki3lrMNh8hI0jUTxFNO3JIDshFqCNv+cBTE8fiycmjcUvPHlhU04SfdL4J9TmgKUcTkJGlf/eonRkBLfiLGcmOyGjIB4gU7vTMtBvRD8hvg+/XK9DQFAK9hvZCRXUpwnQN8tkGHU8C0lZ+vhHwV2HePcNRWjEeW51GyoS7TbsWuH/Zb/lI5pgiPDTKGDTwM3h40kg5rYmStki03ge2b41ESXt9L2mVQJtWCbQqSSDBfesE2rVOYL9EAge0bofEPkdhHVWP14QR3a/BnIdmysy48MrbUNMENEdAOsjuZqR/qpEMsdIYjI6GtBACTJs2DiMqBiLvM5SQQjbnSbvf1O8mDKzqZzE8arF8XhFVT8ELph024tGJd6LvyAnynRh7pWDkdbt9pC/JRJR23LIBdUOEXN5nqA3wk2j4+Fkc/RUySVtMf/w5NAZAU5hXFKjZJwp8wKN17oPmh0WHAgSr38UJB30Fex7yPSyhbZFMo0eXn2HTmvewfO1qXNm9HJt8C83S2d2tkYo1UqyNuLdPjCPuyVTTp09FZUUZEBLizhpAgF4DemLwiKFiHt1JCwEZ+AFN9xSQ24rHpo5D78p7FWyg4Cz+7I7aFUPjC34ncphQZViayVeZZ9RIYQZoXozb+12Ntm3b4pgTz0JN2hzUJjmpQC7VCIRZ+FkahXGYNQDSq/DovSNRst9xWErTPBng+svOB5pW4cXnn8L9815ErWWi4PlpRNFu0454ULSmEPp2yTgn6GJmons6ffpMVFRUIApdZhs+onwG/Qf0xJDywWIt4kMpiqDZ9mESyNXioUl3o2flfYI/2UxSVM/G7vC3g8Mu7Yggk2mMoDHK5vELwCx4uBFblzyHYw7cG4lW7TD+0VdkEjTSFOBdNDVCoixAOuehwaNP5AG5VVjywmNov99xWFkLLFm5Cd1+fSWQWol5U8bi3of+hkETnkBKQjfYzUjSM0WaiBltMpTjnpiJBOk8MG36bFSUj0TEPAIjerQl8h6GDeiJiqH95UmJSWjwBayC8IHmrYC3AU9M+z1KR0yUjyS80x92n90aKYbELuyJCkvzMTXkgE5E+hkgrAe8jRjZ65dol0jg6NM6YaVvmifI01xgyYqFu73QErChELcRW5ctxOFHnoql64DnXnsXVVWDgHATXvvrfBz3owuwtIGhV8DL7tZIxig7MVJcHkKcupKrYkYqrxiJMLTSLprkQT6LoX1uxqhh/ZCNchKObNfKTMhQTYC3Ek9Nux23VU7ABguo7kAxuxlpB3B88R8EeBha+YkBniiLzHSIkljz+nwFEBL7HIun3tqE2pBVc75q9FS6EpmP5eVSiPwmILdN5t5x/3kWNm/Nw6ftyBKVsE7OMRlXiVk+hcy427QzC8s4yhF/i2kXMxIVEGE3bfoslFdUCWemj2ieZzGoTzfcPmIgWC3CsDYtdH4YkUXYADS+haemjkTPkVOxvmCKu4uw27T74pyzwx1EWGBmGk2MfB5eaIWqygXRLNi6EL/pcDISib3R4dp+Sq1uzwMZYtVSSmKIgH6VZKGHbJbROMCn2oloXbCKIYUQnioaeB8RzdKi3YxkVpy4ScxEnLQwkopKiyyHaTOmo7xyuNIODIkTWyF8DBpwM6pHDJCpR9TEjMRMEfJ1QOpd/Hl6NfqMeUCMxNC4hdSNmRJx+O7fdb8DX3zRH0QYqxmYC8oHyEUhy1GRpvSj+SzltAaLXpiJfRMJ7LnHQfjr4q1ChHwcKhvPqr6bA1bTAV6QQsRMPPEXArlsiGyGIYomePlGSVWZKbTPGab9jBKhz8LrZw35s+7/nz1vTGRmmHP+BbjY1HPhaTESU98BpsycjGFVQ0Hzmml06imeKR1yM24d0E3RV2p6MSbNPgTIJNcCucV4etII3Fw1CatdHsmKih0jsVZL9VohM+osp7Akn4JPlIYRjwcqqmRhZeF6d4zilMBkaNGyvKF+xwEUhmfZpu7L50QkIpT4eaRF9oX/WNMeMvoV6CcT/dzYRuF+lbdHOk+/npJffYzbVn8j9dfGw+fT1LI4AKuqVaAT919Y+Cxy+mfn2WdXO0fbOuS0B1eWT64QxW8A8qvwm4tOxZ6JBH54+a3Y6GxsmQ00zcJITMQSV0b/iFwiM5amhGwuqJNW0lm2yzLzvMOLplG0EE9xb2N8EU5kTME3NPgRtkYzToIXw8IRUzHOBWcHx5gubG+0E38Xc8c0o3FQ9bJ8KuvI1sxSdlt0xqQyrxctGr5a6I+woGCxwEpME+wXt4Im4qBdnwt7HsqzeoSlPL4Kf6fMnIphVWViJJUN0WqAjx4Dfocrr+8EL59HJhtKqwV+Gs/846948g/TgfQS/GFCFW6pbmEky00ZtBOyHPM+cmkr7c8EnP/CAkwjYrEHY+qM8TIgFTFF5bLwLLSkb+1boSWLYHxKaB7kzjd+D+AjnWpQ0Dad22bB2yhCkAuRciXp8DXJBvA9+JmkyIlNMPciCyefUSaaUsSL3FwcdpI4UoY5jRApl3gDPJbuKCoToYljI8zzrFXjDB/TIOD8lE8BfjEhfuJ78fUOd6JPGQkkc4dc0jWvZdABG7DkxSnYK5FAqwNOwDs1Lg/EnJP7mGQPoYCDM0XoC3HsdhX/F2KEhaN6htrgOW7uua6fjBDGwY28GqPzTGMGaGIqS6aNm5fBgYiDXfN8JIk9yMohz1HN8lIWb1DgkS0cw1vi0zEzidNFJSkbyWDIsdRzu3pI/SpyIpnE1fK8R8W8TqiQrqjdPV5N2syhublZ97PymmCW/ctx6rOTICkcp/FmlfmkzRkzZ2F4eaUxFwGSjzB90ji0bpVAu3YJVI0YI3nOKCwZLZmnjZEEvFV4bPwI3DxqHFaw4sHhN9bIibhERXfnI2XLWYwntFH68JGMuXNyVAhkvKRUYZBh47mY23R9s5fSOS/LcxwokPFzkgR2gDLXg5drArIpRDlP/JaUJ+gDTXUMZSEMWR8VwcuHSlSSJXwSRNQIP2pGOtuMDB/NbmVIuOxvIyI0I+dbRIyqKudlkCYOCWgiLGe5HM7uUVjTo5QskmoF4DvcfNrOEWhMwMQpYcXoj7QJfSaJf0fQBER+C5BeiBOOaYtE4iD0qZqr6mEKCsYS9FE/nJbJm14iCFsYie05KhaJ8XnGq9YXXk2ku4MO0SxvYXhD17CxJEuXAtQ1GWwzLgQswnT36Np4YBxLmLXqdvaRzJ2y5kjyYiSCU9qUDXD8MSP5yCnJRkLajpkTq3DPlPtQF+SRDhlksfFpzk+YUmkOx9SYs8Q1J9xVVw3GtEl3ql3COfYk87kIQdoVCWvQfDY3E5gGE8qMLMKIPibpx1OwoWxYlawU9ZqOKPsbMWeURjbjqu+lyXx0uLoz5j80BWj+CE9OHYMed0zASmdR2MQ+V9mgyWIcvJdFsq5OiCNKSBjUMBRQsvdzWYV1paHY4XyAx+bOxkFf+RrOPOMc1DVSbQPZiG6YJ6lHu4xmFTUSgZ7KNAnwtEqQZ/1sClPmzMcd90wA0vVAzmrTiO98sF1lHOzLdklOYyaTU/RH8uQNYZRh4LzqcZPyJ4IsNZCPIAgoS8Ssg3qUYt7M6ZqfusnzXFSNhOkYSSj4Av8KVOyQF98qYi8mclLLVlUVzxpfjj3a7I32+30LH28JIclsQxCX53MUApTDgaolYibSOB3MdZ7PcPfFfGhwIbSKT7AfJrxIL14T8eLBq98uXNVxnqCiWc7G41DicXEvhmAoP4kp0+7F175+ME488WeoY0aYvQwzZtY7840UQ3+Dm9nSxlCR14w5s6fjxt9di2RqPSKkRWfULKz4oK+BsBbw6xXNpICh/aLR5JL4dZeLMXnOg9ICQZhH1llPGio5WQxEmDi4xGOwXiLrUZhY8er0GbNRWTVKdJlOJ00IUym46S1+EMFzhkpjugl/fekFzJp5H+CvxCNTRuDGYWOx1tEUZ9wWNBI7kW6qxwnHHIW9S1rjlLPOxqbmZsXTrXLWATfMwUs36kZGlZYueR+XXnQBtm/ehot+dim8AEh6ITJ5aqyMEpS0YRkaJoCpVumf5IhLcUozpk4ei1/f1AN1jWmU3XQdDtsrgUSbPTH1kccBvwbXXXYWEiX74WfX9EB9AKR8D1Euo5yNymqYr1F0K4sp08chUZLAvm0TaJ9IYI/WJUi02gOJdl/DwMo7gGwav7r8Usx6/FExE2HtpQhANz4B/Qv8430F4pZItrYcUolSMYB8mQyQ2wykNuBbX/8q2rXeC/1HT8em0PJKxigkJo6H5EOk2/08p0cVNBZNoE9jJB4jE7qP6x8lMQVY3GC+cTNO+OYRaLfXwfjmCWcjQ1qPGcj5uwXCZL4rqsPKJa+h4xWXoqY+jYs73IgUZaBjJPlFLrRMY08+CaU5CVzmXRLrln6AH537U6zZshnIb8KsKaORSOyDxN7fQKLdnki0TuCAdgkFZAYPGyVcywphvzIZ1K5cgdPPOR9r6ygWCZic/G5aO/bhhSaA9Jtj1/i543EPWT+JdDaFB2bNwfDyEYp4cpw5WlHKAQaIstS8VAYWeSXsF7y1EI9QI4Wr8NCUkSgdbfOUtlOlUg+31NoFCLMpbFr0Mb51yBF45h8vitBECOR+zmPXh501p5CcP2vGZAwa2FcdJo5TWZMHZKRU2KD6MbvNgMs6ND6a5hjhu27NIpxz3qlYUbPZEJmqwXcP3Rdndb0e9KKQ24ilf5uH755xPlZ6ZpMKNrGTrWncHnxKcUSYOmsWul7bUZUBowdejz4DKqWjzuvyS5QOZm1VgFUfvouzzj0Tq7fWSHY5WNvwdv7Pk8Xbzuf1m9rMETYBJvq27ESsTYyv6AhSJTTIzqavdPxZHbEsbfNdaH7azcxZJM0xd8ZioY9kHpkgznQs6p51zfDT0k3+tohUxnPmLmv8Mluxbtm7OOiQ/8Dr79aIkRiIUT81vZ22g1kjqn0OV+Dx2aPQd3gVttEy4Bhp7dF3zXE+VCQBSS0iDeKiOvzNeUDIbkVF6S3oO/L3wgeCtfjTnNtx2RXXY4sP3FI2HOUj+gOpj/HLS36IvpW/V3Eug5mxX+6ncyjtMwCDysoF4rh/zPm0jJ03cNsRMDSSGUuVGYoIU6ZOx7DhFUItJ/zJtSFsiaI0J8OEEtjNWV+BrueffxaPkZHy6zBz4kj0rb4XWzSFgjCw4lYykwUbsh62LV6O7x52NDY11WFTLo0sbWP3ITItPxKB/k/opTFoYB+MrK5wMzUt4MZFJhjETUdWP+bRmVZUzciEtrBPAxs++vfrg8HDhjurPgIaVuORKXfh6P/6GdYTHsnNeOrekZg8748qydCSIfTJGK3h1GzG3pioJPDyIcZPfwCzZk0E/A9QflsHDKy4G5sC4P5Zj6G8ulq+F437QYN6YsDQ/qBZwRA0aehTP8VMVKDmna8kjJxIZ59pKQpl9OhszBx/uj7t1Aslwsf4/jf2QqJkf0x9+i2V6VMUMHSutqKU1hIQEyjMxmeaKV1gJGeOxl1Ur3b4wSPG2bmATrPkiE3djWpRv2kZDjn8BKyusRpbu8CeQSIlVGnay8DKL0J5747oPfwObOViLexrNpTZzH4xmkZLwwx1dpUUGbhYExlpM7556P54ZfFq1NIh9Ndi3oQy3D9lrsZ+Q58+GFbZR4z0+LQx6DviHmx2zMohyK3IA0vefgc/OOlEbMt4aIjIHhaJE2zUV9PkO8OCV/oRzdtAwn3ipCkYNfp2mXbEFV0bRU8pIUK6Igx4G/SIkddfewmPz5sKBBswe9qd6Fc5ViZ5k+cmYcYaiVKFEbOGxSvw7QMOweraLdiaZ4CghbNplzZ4jKSZM8yVWPr17Ymy8kEaEP0VjYVRGD+JTGQ2MDvkcdYnaU3kQEZYBi/1MY465kQseKdGBJfJckWXWqxd/AbaHHAkpj78DNDcjJ7XXoONdRklMbeGJE5FP8CFERh6pG0da6SZc+fjnTdfAPx3cfegq1Sly5mMf1uwGAOHUiMx35PEa8/+Af91xg9Q57u5JhymA4bMERLvf7MVX2vXG50LYIpARfAkTijbnH1GmuTG3E+wBkO6XYL2+3wFR570Y6xuYobImXiUjNJwLqqom9iIY6RY+8m1N5gWeHwHRnIMLo1kijXn0ZGhv7YOyxe9hGO++SOs32ryxTSvkQ/J0bQL22DwxwTT4LFTVKzZ6JQACVBBpcjWjWAdO5mJPnEcYmfYes2q93Fxh/OwPZlTvArRVjzzxFS89Nq7aMwDpWWDMHIUS6C24O0XnkKf8mq1k25kNYeNkRBo3rASZ558PJZva1Y/pPGUvyO+SK1klmxL8IMgE+TMZ6NWZt94jFMnLM1DjW2VKS5UpykxbI0Cg1X9fXp0x9mnHQc/uQH5sFHHG31zYwTymJGEJC+H5JI1OOnQo7ExWS9JwbAkJY/vUfozAMgcSYQgy4LMHC7pcCGGjxiGJBfvEKHQwaSNmZNpQHhzI3p8ZeEJDg7/Paxb+Sf85Oe/xEaWo/E6n+sS1IvQL+3QGZdffh1eef5N3HxjL6lX3sVIG2eOKIEpCqX8Zut8ODUmVV8jELyH4d0vQN9R48F8DTUZnUc9Kd8ENKzBD/7zaCzZ1CgTgpGhOP/BfZzPiPc7M9WOjGTIEoBcVyjRyUhyoNk/SWjXTSZBsmvQvPoVHLxvGyTa7ompTzyPzc5XyhBQzLl5DItx8ZQis5pMIO1rDxIBOFknAAir+hY/zPSKy9UYxWTEyFvXv4fDjjgFaylpSDWRGUv8T98zBwvSKBySfQ83XnYySivuR03e4ElwKjrKm8OMTDgGB7iJkZQPZKAqh2mzJ6BDlwtBYWxwqldQgaNg4rpX/1KUlfW2EqjQ0h6NHDbx4lmYXozib8WvOl2Av7+7TMW/BhnLPbHqPo8kV7Uo5AtZD2yR/EDCPQaPhe1tnDzG4ALxrqCTQvpmUZIuRTYMfwcUE2ZJZXzTWESl2neBloSqlgNg+9srcPKh38Gahho8/c7LmoB21ZXX4bnn/4qvHvwVHH3SSahpapJknzHxbrTfk5PUStB630OxaRuPbwWC9ehxW3e0arMvTjr5fKze2AzG07LKVbBnGSC9AE8/XI3zLvsNavNAg9aFowROA9ntWP7GC9ivpAQ33FKJZ9/coIU+qJYzyHFBKuMbuhKsbysmJPpOUR0QvI/K0k4oHXGvpgpTSmrdsoCBBZZ6rEbXi8/BPz5cLZORTGoAsTBmzDhEjmCT9xEoqEGwGtMyGqiEoWNSLZvmOkNhQ0ZyIkKUKmEU356rBcL1uL3/dcornd3xOknY7Qznkw/YDhErm4ZPtDipPduYifBgc/zFUI4+EmYxI9l1vIL9ZIpOkVL6X8EKrF/5Og457BSs3wAsfPFZ7NMmgZ9f3RVPvvgyvrpvG5x80rexNplFUz6LJ6YMxoGJBBJtD0Fi/+9hWW0KKdomYQa9b7sRe5ckcPx3v4Ml9dvMt8lQk1iQiYJu8szxGFDWW6a44JkxIcHlr8gMpX17oqJiAOfsO5gZPqgxOHIRM6czBGvQ9eenY/xjf8fa2EeRoOHAOdO4AY2Nm3B5l6vhZcwXJ14ZMaZoiGnFAmgRstkWDcVzgm/M604j6dmkWUWiqSRMg1EosG0yoeXR5CNZbqDpvTU4+bBv4YP1y1GHSD7HHu32wYyZU5AO07io65XoMXiglcOEKQwa3BfDR440W5hj8dajos/VqKwYqiWpBgyowvdPPQe1vo/6fOScVIZSF2He5AEorRonQqZ/QLnDpBmnZqNxBU4+8kAcfsKFWMtZ1xqNZ1FEKvCUW8SP9npgAQzBgRGMaDsQvo8Kx0hMAdZzvTjRGj1l5nNW4IaO5+L+h/7cMtNRfpxJplgTEelkoCOPOAT77bsnOKO1FQkqkUCbNm3Qtl17JFq3R9nIu03zUvI7Ukg74wh+MwIuREiSV3QRyHGRx2Ar0kv/imP2SSCx/1H4w8IamTNk+Gzakw9ITFFSkiW4kajsG5FJqWjnCowUU4qA0cJIIiZKZ97OXEm0HBtWLcChh52OjSxjDurx4PQxSOz/ddwz93EgbMS1nc5Hr+p7ZJkgtwKVt3VG/yG/p6FnWhwRevfpgZGVQzRlpLKqDMf88FSsy7qgBpmJAiwKUDq4J4ZU9FMux6J4NgzrYR59+/VCRfkQZ63Q33WmNs00s0GAiHnHJRje43L0HjtZloaeQF/J95TyGDzkFqTTW9HjtlIsXboRPXoMFPNkwwy4ETz04QlHzuMi45B+CmBzX7gjHg3mzAPz2gxyPnOkIiRkMp4YqFgAJySyAqD2zQ9x4sHfQE06Bca0pjw4E987+SQkUzYVt9vAHuj0q842eS0K0P2m6zF8RLmkPZNYSG7CD//j63j11ZclWTdtWI6vHbgfnnvnYyGEHQMX+QgWobpPZ/Sqvl+rtFAOJZXIpVe+FcguRVWPa/Hdc67GxymX53BJVhEDR0qgEGnOV9ABpdC5uN+7KO/TEb2rJkgjmULOKzEshKQWo7xbp4Lpx4CqrEJXWkSNRGZivkVhUZYAqQzIfCf5RU5x0HdOuY2IJXEnVbRKP4GSjE9PSXqzipvSS3CIGoDmd3DfwF8i0Xp/3P3IAtRSaHAgQig52yqQqaW4EblEpEnFmJGcxC7cJ0iYdNWTzCfg+MyfY9JxBTaufgOHHf5D1NC089bj4ZkjceRp52EVuxvVoaz39Tiz841iHIZ9h996KarGztLSVM3Io8HfjmOP+w4WvLFQ5vzyZYux1yGH4MX33pP/wTEQZHzuzaW/RfnoQYKBzCdqx6xpVJqSffqV2ozVnRbUVDUBkhKx+p56F9W9L8et1eM0jaFJgsHBCx5WrX4bv7+7Er/77S047dSf4J13PkY2YK4xUiWM4xNpImKBOSQ7ZjBq4SiHgrhppmyY/2QIPZcSDnWtA3Vs6ifEZGkf3jL6SN/A4vU1MjXGPzAJl3S6WD4Yy3junTUBF15xgfFqFGHAgJ4YVjFYKo5tbFryLi4750doSKdFUMiuxdWdz8O4B/+sYAFtTuSSQHYxht/cAd3L7xBAiDtzAim164Ht76LL2ccj0f4Q/On9TbKj1WcqTiodqnWPpiAQ0mZxH6u7YtCCjNQJPUdM0GovZDeirTlDaZkEMh9ibM+r0H/MRCXWihnJmIRUZwWhJkFdOZQARZMgq43qPTbgyEwcHwmDx4KAnWUOhqPLgKuukokYGFL6gbHK+nex6K/TkdjrYDy/NCnNyUy/ltUlFbKtwNqL7fViZrLsnCMCwiCmFMHDUZkzVQgzmS4MaxQY6QxjpOwqPPxAJc69+nfGONnNeHLO/Tijy03S2MAaVPbugl5D78VGxorg46Nlb+Ds8zugmeZoLoUwTKHLtddh9qN/lC+U48J+1K6ej/5De2Fwealimeqaq5CR9s776N2/FBWVZS7kb14vz8nUR9L5mtRIH2LErR1ROmaCBDDxRsFKeCUbuRxJAyZMGot99zkAr77ygQRPzEgU1IRBItEW7dq1QUlrsyxoXTD3mChphVaJdrbR8pD10RatEqxEKUHbtnRj7Nq2bfbUdb7vWw1qIdhAmPtA3bsf4XuHHYMNTU1S37HUiz0AACAASURBVHdNHo8rru2qDlExjps7BZf9ojOymWZNkx40qDeqqoYgZJmNl8Obf3ka5/3wNGxKZdFAzZNagd90+jGmPvp3i/aQwkhY2RUY0+cX6Pv7yaqilV5RsSx9mHqsXvAXjB5wG8659DIMGHuvjCQCluks+qtGMGYqFROPnFwSSrAIFaVd0GOkSS4xUkTXn1piO+AtxqibOqHf6AmFcvidNZIYSclE6hkfIYmFk+7ETCIHPZo0QWIioXNLhmaLG8+xII0eGr0mMyF4fSoXgZl+Rs+qbr0G53e5TgSriBebJhMx78O2uACHa9uYiBfEotiYSDwiwotFaAsTsZM8L40kfVdvjLRqIQ47/ExjpGAN5kwZjPOv7W6MFDRizsQ78OOu3bU2AXLLMKJ/V/QaOV54pOn34Zt/VzK3hp2mmMo149yfdsCUGY/wgPJLKa1yBPTpcwuGDi1VCEpijwNhNI7mvhipFyoqh1qOzPl+YiQGaVR7xwTlFiC3HHcNvB7dykYX6Ibjkk+LDJ7843Rcfc0l6Nd3KHr3KkddY66gkbKudpTCTGsICqr04+I1B2mFuCIXOT0WqdDXmORIByqMNp9TA43NUOWRmKRL+8gsXY/vfv1ofLxpA2oRYdLcubikc2c9ssnPYtysKfhZl4udoeHhlu7XY2T5YKoFOcbvv/AcTjv5+9iQcctSNa3Eb84/FZMeelq+UL0G3aziv7JundFj9DjVLDWJEghiLnlUh4G33ooP31yIqdPvw3dPO0FLT5EZaPbItGOsjoWMHDhvc5RkURz6H0tQUXoFeoy8TxqJxYW8hCYRvM2yte8qvQbdh9yu87JmCpE7V4EcepoDRAY65huH4iv77CH/KPaRKMlalbRBok1blFeNlhHFkln1gVJS0oHmodhYLnM6Z/6ctF6QxvaP3sSxX9sHz765WAloLrEl1lCpihVpElmfYCSNN2amwvB3YLAYyQVGUtiXgmC7fKSNq94SI23cyIGvw9zJg3HOFb+TAEWuAQ9P/T3Ou+5mY5z8CpSVdsbNlXdZotzbgjf/9gh+8OOu2Eh+J3H5Tbj6yhvx6CPPwctSV/ryj5qySZQN6o+7xlbLq2MtpuQAmcyVjpX2uxXlFYNcBYIJB/0XvRBxDF5sAzJLUdatIwbfNUE+EiFrwjNCunkrBg7+LZqSNejbZwiaGoHhFXcq4lsocGaRLquAESBH35UT9opgaTRS4JqC0OQdikpzVL4VcytCzOdrEX0LUpmP1JxF8uMNOP7go7CitkaSacLs2ehwxVWqm+XtUx6ajYsvv0yoJSEPGdgbI8r6AXTG0k1Y+OwzOPDAA7CkMSvHGant6Pbj0zHzsT/JV2EAI2I9lbcMo0p/ie6V99m8HDJJhvmKWmxc/j7Ou+haNGWB+nXv4qC9Epjy2JPqT+w/cC0ylS5SbHGUBDjbEFAYmv8Y5aVXFKJ2DFyKCeln+VuAzCKM6HEl+o+2FTNjRjJb1/lBrHZnoSZXQGUJv/Yy2oxGqc716Dyo4um7JPOeMRIfxktp3hBpOdNSmSCv+kA5r2EG/bt3w4/POBtbaQ04rePlkgjoJ9IEzXtKFn4qI8VYj/fqFQFRBBB3jjuLgMWMtAoxI21mfsBbiqceGoWzLr8Va6lWg+2Yc38ZLvhNLyxnc1iOsn6Xokf13aahwq14+7nHkWjzH1jN9VvIItntuPT8azBj0h8FF8v4WbRs3LgJGDp0qIDC8fECEqBSDfDRp193lFf2s6Jk2T+mGaSQYmbiVO/kItxw8fcx4ZGnC9FYBag4ZkZ8sR2Z3BZ0uORypOmKu2R7XFHDImdpOsGIA43MNVBZFJnfGeZOYzL5TXuAWBQmGen3GeCxLQa5tBY1Up4FgEGETe99jOMPPxo19VtRjwATHpiFS7tcqRsZdp40ZyY6XNlFAImiNPr364XyoYNMz3kZ1K77EAcevCee+2CxzDFs3oyffOebWLmtueCrKNOQ+RCPTKlEv9unGSMJ4RkguxEPTrobEx9+SStfIrkCN3T+IS645nqsI31TWBARaELKb8Rx3z4dr/3jY53gOQ5Wsf7gY1T0vgqlVfcJ4Ex20uSSuZblIhYr8aufn4oJ85/ZQSOZf8Q2YoIkFRFJbu+IlGF3foxASRAWRSMq5IuQ8rnxAqlN8+vSgS2ywWnL21Ytxv77HowZD/5ZyVgyO8lcNzLUGjIowBG1RO34VDXJf1TNrrrBDvKk9ZWnC9fpC1uJO8Xyo43YuPIDHH74adhSQ19wDeZMq8C5V/Y2jRRuwZMPjMbZXW82Hym/EmV9LkOf2+82DRVsQ+PqxTjoiNPxzOtrlTf0M3X41rE/Qn09XzfDKRZMeGQQhCnMmzETnS6+WFUoSqDHfhunKeRT6Nu3O4ZXDkAWLAJwCXeafhZvMXRotsAq/KrD6Rg//wn1i/CKGSmvqQ5Nmh2g8dM/c4Xh/E3NlTW1giC7TVFACl7mSi3dQWHHjfBwJEBNC1+VN80kATYU5ZHOB2hW8Ekd0DHSTkL5ikwaW1auxlEHfx2vvPqiBlTavx86Xt5FSU8OsFf/vvh5x0slfVl2cvHFF6FixBj5Bgzb5rEGfft2ROfOHZX7mTBxGjpc1VXIoQ4h54K+QfQRHp4zAj/peL2ISH4PO9awDid/8zA88/4aMyGSK/H41NFI7PUN/OX9TaDJseD5uZjz6AQ0530c+o1T8dbbW7R+AUlOwYSwURJ2WPdO+Ok1PQtRQQJIuSCu7tO4Cr+68ExMfeI5mQgsy29hIlGzg2QM0eJ9fN72HBJhTCYyv4pr2jn+c+fEXESaz0UGWeG8Fo9MGovjTrkAG7LSra4NxwAUxZKGsQS158eCUggtMLeZQuqNGMnyGmJ1dk7Itw6xrkz+QZjEqkUf4IjDjsPrr7wN5GtQ2vMXuKhrLyR5fbgFZT264rxf3KYELPzNuObnP0CvqjHGaEzS5JLo0asnOl55uezYqRMn4KKrb1N0VviklKct49ej4aP3cNVP/wt1XrMStpz1on5Ry0QN6Nv7VnS+qosECYlUHJQL8corL2HO/LmWSeLqTOk6nHjsEVjf2FjQ4GZFkPg4YhtnPGzuDS62U9KVAipYp3B/adWd6m9e7z1qAqcFUeOQk5gjoxDgNB5aLIwQzpw5E5VVVoDA4JIiu4K5FegmmKnzmxvx7aOPROtEAmeecRqe+csftfonVwCtqK7EC6+8iDbt90Crtm0wrLIC90+cgNZt6SPshXsf+LOmKnDyWhQsxs/OPRN7tNsPx5/xY6z1QgFPngL/0S7FGqxc8Tx+ctFV2EZRzPlC27ah2yU/wVdLErjw192EwOaaxTjqKyVItDsU3z79QqCZS5qvQTPqsCUM8J/fvxhrNpqTn6Z0JjkmN+O5adU4sBWryA/CzWOmYzunTOQ995IpD9i6Gv913DFYVNMgCUvJ9klGckj4jB2RZYzERlzWkwfkl5k24TWppIdISb06oG4JTjjqYEye/6yimRJCzlQU8h0FFBhHItISgWIQ95tPpklpx9zznTnDLhjDMXqY1WL7IjWG5lNNOPbQw9C2ZG+ccvKpePn5R9C6DSNSX0NZ1X1464UnbIXYdgeiX/V4PDTxHlu8Zc99MOnhp8yGTiWR9Wrx0wtPU6X9OWefi2XbA9SwJpbKn1UxDDix0qRuFb53KH3BN8UA0rySLklMHT8arbXSbBsMrR4pN0KTLVkIm2Mqn/VxNilvwUsvqdaOU/JJRUzoElSxaaUfGjZhQnpwH8e4WnMw4kKBSzF/2kj0GHGvCWw0Yfrvh6Jd272RaL0vEoJFAm1LEio64IKQfIXB9Dlz0Kd/z1hOIusSyxysaSTmTFh4mKY5ESCtGnlKOhYFhshwWVxEaEwlVVjJzrPMgp+UZ1OrtzNBJXm1XSvgEE4cLA0x8o8SohwQ6/qibUg3r8MJ3zsdCxYuUzW4xp1hJKsZ2wJPUgA5apdmmWycngF/E6655GTMeHI23tlUi6tuGKLaLSUxKbH4xIgmIu/LSsLR91C0juTOmo4QePelZ3HicccqiMH+UY7tKiMJCI7w3c4YilXajtB1DQFC8yNbjwcmjccpp5yGLc02eY0JZ/kDjvatR8YgYhJRi+MLAYpt0363Yzswkqq3jbntAuLJFyOxsJRvupDmJB5oPvn0E2x1IgpjWq1mPmc0c5lQpcXCxVc4hYWtUV5AK8VSVjciyNLeYHGO1dolVW/J25hHYyHhMozq1w1lY8ZhG+lCz2F1A519GwdRk/PDQoK0OdWEyy79OR6dN1cPZHK8d++eqB49UuF1pgU4bnZFoI3Da4LJ52ck9pwa+MkZY3DRld1QEwC39OuPMdX9gKaPcNXFZ6L/mMkyJe99YB6qx45CJMvCJlCyz7FWTKiegqaEe4cMgcJCVpZSqLCPQMoxJmUVykQgbU42wD0LVpOcSAcPmQy7ZpKSg2zwWEUeqHJciGXZS94D1Wlpr97QTEUOho1ybknAYtdAWz7LsqPAZliS3L21eP/l+Rj36BxUP/QHfFQTFqYpa4KZJsUF8D0zd8j8dBUZVVLyllIy5aFXr14Yc+cdYm6SZAERovhd+Me+O6Qa4VMdufC8gxVzLfBqxUjfPu5k9C27Q4KGZg5TYTEjtbRkY3BN2+HCf0qklvOFa/TMIgbk9Ub1yEeecKBrSeA0zwo3MqjRRFlqqxY5YuQz+BRW8PNSQpNRK6KQ/Q2CJPyATJiR9iDT1SQzEmBiuJxNCCShrv1oIU454wKs3krcEuY5+CmbZsvaNdGWe/Un6YXC79m/P4PHyEiBhxXLP8IZ55yF9Vs2ankCajzek9Fct7i0ywBkWvrzaSQxUma91mmfMO9vslBu6T8QlcN72IvFZt6JbgOqdZyMNGxEuRjJhA0nNtqzTSNxgpALorNI0vMycqBpUxMtsUbi0k+UaIpCOQng57LKsZB56IRbAjGQs5llKQxVMxHHvXuXjxKOeaBm7Uqcf+45WFdTb8pCdVOmxjVP34XVJXZYa9W8CItenouJT/8Ji7j4JXmP5T9Zy9XoDVwRkHTrYnNWZOjXOxVMKZDB6lUrcNYFl2FtXVoaVpI9noRWINRd+xJXHIjqiGWJSwZBSI70j1Zh7riRKNnnEKxpsIoNXkaYFSo2SLEFAicxmFAqHHLn9Vt+adHlYpqdGEmd8JUDE4HxGgVeAqST0jeFvEq8kEhzMisCiZcgYB+5EixZqamReSjrYyZNu5y4pS7ykQxs+gVNNwrhKMOQHpm2UTQyfeoM/PqGm7WiLHHEdjLNSTEWq6zV1TzXkcjqPVMfLVmEB6dNh9/UjF9c2xWzHnlQ6wGKgzjzmv3iojaaTOiA4YTA5zXtbKJvEx6efheeX7BIQa6b+vRFRUU/RZFff+5JVN85TpbV5Dlz0W/IID2Xwpe0r2LZgmkn/yJSpXGGdp/MB3bPTDt2ihvrkrT4BSNMXHeNopT3Rj6a62oFX9KOFnYnVBhAkHrnUR/pTKO0A7nYYy1W5GH8PXfhtzf1xfZ0HipzD1OqAlBxI0uAsp4Fr0LWCr2PxS/Nwvgn/6JoH9knw7Ibmi5M3LhVNDh1Wj4ZnVmCgMDm9IVcEtd0vQpT5z8lJuT4UqntbkZngVS/EBc5mnK0byaXfpAHiGkydkRt3qCC3msvPRsDR9wNrmtHycwpJpwgJ0eYTyY9FGiC7VGwEBfuE5+PH2y3OJwZ5/IUH20fzt3KSpiRuNUhVZe7mjheG5qmYWEtGbrwcmkxsWn0bI4Lz/B+W+jG8ziNP6eqBvp+jJrJPMtTkFGAumnQLAx17ZNO5j/0MCrG3C+tJe+Aph4LXMl8bqouR1GfbcYrL/8D82bNwujKSowfd58KlhVHU1GsCVEb0a5rJNZiSmhHrKBQrxVUu31sFfIePaOMaIzPmfrANFRU28TCNAdLvghYseJ8JC5sEhdqxtOESdREIPdEoyW+CERf0yqI2UBza+I4JaWQJSRTEfMHzvFWzZq9SZrvQ2X4k0wpRuF8+QgYP3kuRt09wUXsaTpwOrkr0SEziSg5eWghlrw2B/fM/yNWOmYhEuSQUvJpNRT6bQYQ0zYsq/c0vtFV5Zg5YxqafVZn02emfc5VcAwQjvK+0I7gJJCJfDkYNJHd2/lMHtkVJLYFzz2D/fdujzW1jZJwZCSmmmTW8TK1YiF0Srx4VhDb5kaBIaGh5LEzIvhdmHJrJfDZPCaGI4GJRNUCCZ+h+pzzhYnDVKrFxNM9vJJOmwicddN8Mte3sCnY1q9I1S0ashK9hACrOEwCZMFoF1/uRGFKurDZWbIJOR2KJrYI2JYEYw+lkUiaUYR01gpMX3rpRcyf96ATElydKtA5Ppfg4cZ7Yx9FB3TcBD+v04f0w64xN7hTsIEv+zBvh8KY+AjQr38P9O9/q8ZEIRBDcMYM5sP6ypVxQzUhVygRIirc2nbiMle+bpLQpGEcXFDHiKh4Gav4KXIJ+PpHl8CKjX5NRDLNxeHR+vUYEBDgyWwmgekr8Jxmn7CXiibTSNAJQFJoJYb26IT/POsCrGZVElvh690Lkpba0dpjt/g8Iz4SQ4S86vNsPDxC6ebO7HKwgchUGzFWqV1cwkBylrkgSja/CR0vuwJDho9Vv5vcclgq3VGf2QAZhb0yG0CJYCYi6EeJOSz6SKHGY8qzUI65dQPjiWr22/JbynERt3rzQrxMgO25whI/4jUSpZv0JgHgimvZE+l35lTYD/7UiI3wbPz8H8/V4GRLpu/dTE4VjRKznGNlOSJeTcYhjshU4rfQDGOuwUATM8h66NevD076/olobm6UVou5hzO1afCyHa3f7Qh5VxiJNbu0wZS0dS80KO35O3t/Uj4U3CUm8sCMGeNQOby/jdUxMdfAI0WZjyRwtvxjB4u3ljPuW/FJfjdqNM3gZBiBpEYI+Fgi6OEkE5orjLC53pggc4RtzMBzbFrt8Do9gxn/WpkFirYR2bTFY8KLb3L9s/vtLIdrZij3bMycXjKjnvGJQX6xA0QoO0z5wnUBjXT4mLTyFm//bT5K9j4CzyzcAEa12IPYTLYEL3MXWYXpmb/wOXeKmptMwKSglkCLQ8FcwDMQAXKMcWUFj9m0citlKcCGCWNxS4yMor2DlYAthDjAazgkEfaUULLgCS+33sfTHYrgR4Gm/JcJK7WpKKJZIfrNRzvNypYLdFIkRiQh5DeaRhTu2Dfe6xic9+pjHdrhO683AeCucfcpWpnfDgTLCuFvaiSWqLEfbIoaqH/f7hg7aqj5PxK4pkGnzpgoBtNMazcdvZh+PrGIfjFsi/vpumVPLL7IddQGYBGYAoCKruNXk7UWUCgAVg9hgYahKManiI0DZGOEY9SkTSakQqi+iM9MKZdTYCd3eiZJ0QBLYqAWNKaSjxIjszC4L/7FEnNmThlxREj7ZAZ2mlXeH+CGTmfiwstvLLygilFOvcJFBJqTw88gjqb9c43vfBJ9+wzE7DnztFafTcUwB5vrVCjZSbPELUzDEVKaE1Q2c9WCFGRWleLQ8YoBWxgie9vCOAZ9YxjBUNfzmqLrdK8z5VyTalow573GYMX3E/Y8bXghsZjGYl9FJzxF5532A31uqeBQRG3mN+9pYSTewx7po4Z3/P5FGYmrU7GPqub3PQwZ2BPlw0oVM6ApG0Ng8oxJqKjglHj69xSVJmLiMXyCkVy3PseOAzQACY4xrD5zT8C23CvgW3HIDhKKHY2ZS99Zwc1VXim5tfE7cxFW2hEDOB6YbBEVRhIYhkA9ixdIK5B5jeA+x2D/ySVs1013ZrPSkkYEcn6YzV/1Go45uAQvvvGOKjn4ykqrC+RyYAyIpJDL2Xp/RmxNaGqqQefO12H2g4+phmzp8ldxw297o0k1n3zDX6PNjeFcG89NWtOrGmn2cWWlFJavXIY777nb5EYxghx+jESMkYweHYM4nLYwQxGuBAXz1XQPT3GzBgxGDr6Ci6vmttMW4SPuiU/utTnTjEwk85KMRNMvJL4L2CwISLZVdLQFL64PbNMEpzsV909MvqNG2uw8UVt62GBX2ucWVaPTXOMn9k0nzZiCsopy2sAF+WPjsOd8SUYyIPORn7ZxwJ923LrIDvB+eTuFC3mO93GLmYkMRT+BgObb1VijRTNCG6uD8yaN4+vVvhLNdBaJOD6H0pJJG+sUj5m1bWMwcOzKf5tWzTsJey0IQheW61Dk6jC4tBs6XXJBYSwME3M8vFZTM6ImMRKDYnTjAlaooxlHHXsaXl2wFEAtXn3lCXS6/AYlMzV+NKjIMu2TiUziM2iZVKGsj61bNuCwIw7F8KoRsQL+BHwNui0mGwnGcOGQFoMiRmD8W6Y5mcnGEMNTMOc1BKdASiHFLW4vZiTDrZmNFpwh0VJzxhuZiBt/f+6P68AXZSSuJsVABE1rwqT/gN4oGz5Y/aaWMkGdxYSZ0zC4coR1XvLHGDYe97+AkWywbJDbzswTw3HncwYgkj4RaIEHIiBmoPh6sllMePQZWCAiD4eMRMbQ+s2mu3gvv6neSxLIllCWhGKD7KqziXlsB8n1uTHWciGbjOMuxv08Qv9ms1ZL2rR6Ab72tSPx5ttr1E0/k9b7d8jANLtok8v820Fy12LV8rdwwMGn4NU3aqyA1d+ucWl1Zgbv81td6MZgzkiXQYAVBWnMnDYef3rmaQwZXq4AgctwaPyEEbcCIzm8GdHHWsmYk6OJYcbv+q04F81z+23HWq5rad9MaHeTY6pixjBGIxYUeXOL4pN5JDDdywVa7ndwjx9c6E/RcR3bCa8O55/mI8UayWiQrkIGvXrfhMoRw9Gctogd76PoG/fADGMkLq+taDG9Vwt6sAf/IkZib20ALQRqx4oBQSAX4CAMEGFkJueBW+RVBG6SpQXdtmorq3G5ZporBxAVW+WuAaPlevvtIoRsv6g7fKL1hQe/3IfDYOTJ2idzNwJZvvhjJYb1vRYnfu8sbGEBAB/FlYFCX0luCwzEWiBCmgE+raJSh/49f4tE4ht4/a1t2LRyOY7++v548fWPRMKvL/gb2rROoHV71iG2Q6vWJWi3x5449vgT0JTNwtMLnj384+UXUTFytBhJYWs3fo6b3bX/fH6MExI2xRY3x0gcHG9wYCJOLGBs5mQBlzwubi2Cv4Qc27e22IzBXI90jVpPZEaxk26dCpl4VNk8JjqJ+1jobKHfcWst130xRuJoAxYPcOoKUmDUrqKqXIKJ05estWJGYvLYxrQjI8XQ2Hlf6OGOX1ouc5ih+iXQ5ETSkWTAwVEWgevEIe8zx98kUcxRMYAFZd6m65nB4B+fwdEwnGqvm2G4UoFbneIVVlRkUTkDuLVJEBT1Q/FiAwCP7ojUHcf4uX9xUFxqLGNr7LFWOExvA7xtqFv5Bo7cP4H2bW0qMwuAOW25/R5f0ZRnTnfm2w/acJJgIoG2ex6IRMneKgL96h7tkUgcjpff3KBat7tHVmHNlpwSzSHnRvFdSVEGKRV0kgEtCkmtJDzkmvHyq69gYFmFMZJDFfvKsXOz/8WMFDO1MZLww/tiQHGssh7IRC0aqaWd2Iy2W6yWTpiKabzwVDZp+HfBDf52jCTt5PwmMZjuKvoXE2DRIX1V/9js52ckRe10MxNenF5Ri943X4Orul6ppD1rN0itrNkYP2Oy+UgU3i6JTEi5xyJhA4oHVrTfuaPudzwOk05xNIUQJxMZIxXO7cRIpq2IMAfAgpnlBBQhLLuYjOR8G/k1poH4bFqyHID6rRU3ea2LFrEbDvcxs+iA+sFn/jPpaPd94f/sEK1Sn4RMk5MVA3R00nhk+jgxBd8fyzUAuL51olUJEgnO+d8DrUrsOCvu99qzrdYpLynZB3u3KsEeidbYY79jsI5r+uZCXNOpMzbXBWgSB1A4pZENLcxu67QBfKMHIWtL8Obw6htvYkj5iBZGcogjiI2RCCz7xlOxeWN7PsPhhJfpAn7hE4qCDbqPbfCY4Z6/dLmWE27RXDpYFAWz3+45bIc3UQE5J19Nf9o/d90nTrk+kvbMymlp09pmHovBho/xyLQqVX+TkZRHUjF1A+ZPHoX92hFPCQwYea+YycaTw6TpkzG8YpjZ8o6RCA099suZdm4ohYFxALYVBlk4Z0fsJxHSAsAdLin8MILnT11bdD0JQRKNpwr5Dtee3WBwKxqkjfafPNO6tuv/ORz6ZloamMzqa9WZKzp30LQUTU8nI8VbgszEzY5pma+SBNq03QOtW7fVPW1K2mK/rx6GdRvrkMv66NyxC5qacprn9fLL/3BTDxJo176tLQuWaIMTTz4VjSmuZW3Ifem1NzCwrNyYy8GC5wg7LgpCUNEfiTVAAc6E9T/9ONztcE3LMcN/fDOPG6PGR7gvoDj+UXxyF747lFv3XaWFRIqEp43XEsnk921AuBiPTitHr6p7lY5gOZmCGly1VSs/ccpLpMgqo6tGiRGmTJuK6upqVfSwIIHPZf1p/PkSPlLcxL/33hKqhIFVzVt+KEJar6skIcVbC8EZ1u2eljIeO5/mO38Yt2uy8p3Fi5bipu636Vgx0XOSHhc5jKuz6V/ROiKjcMXWF19dgMpRt+vp25tS8eutkMnSWLF0jVyQuDJAT/i/96/ASBw4BYPTmLGpT+g3Zm3CIxdugf8h5o4fhF4V92gtERqfZJx/tnHFKia9p06disGDB7s1z63etBhauxmpGBq78J0I4MqrxhwxMyTNxCow0Y7nixmJ3xl6ZTKWTKUqCfaD0jDkIu4LMWTwcMyePbewFFh8T9wOl/8iQZEx4iV1X3zlVa15Hr9HKl58hUtkSZrqdQ9mSn2mObULcPn/65ZPMlLsg5vbQfOroDe8eiBcjsen860S90sjfRYjcRwUlnPmzJFGIlPxN3HO7/FnNyPFkPiS+7hyXmU9zGHRy9O6C/bdiL5YK9kDxTw0K8hQmkpiNWSZNIuJgZUr1uKQrx+BhQvffDbxawAAAqdJREFU1g2sgKAmIvMpfM5quDSnkUeaHEcNxbd8tNtrbyRKWuM7x38XtfUNhRezkfDSGXmZYBV3i5b7kgD4H7q9wEj0raWRKFRaTH2+VUUY4GTAJH2kdXhi5lj0qrxDa3p8FiOlOG8KwH333ecWcbGyLK2tWDTm3YxUBIxd+UqNVJw81AqecV2TfIli5uH3nT+2DjUZiUzY8j4ql/CkqeYIn0g1U7KlTfvNiXZmsrEagMTV0MS31LFV84f4XRXUaXoFLU79/3uMZH6YBi9TN0KGVdxMLjJvl1qP2eOrMej2ez6x9kMMi+I9YUUY33///QWNJFjz9aSyRAyfuxnJ4PCl/nN+FoHPD7UEtQy1Czd+33FrmVXJe8g8LPOJtZHV3DHfRESRSXmNhK3aN8b13dK7ZlZqflgY6mXF7AWTvdzTGY73MYOxET6Xz4m/x33Xgf9j/wzqlBjUQqaJdEwyy1IgfD8KPxGn7rDiJEyiPoRe4qAQdlG4nbAo3shEKgzO522FX8KVJSgUTLtNO8HhX/avGKDUAWQmMggZiL933HZ8rDEQAxU02bI7SDleqeWPnVSM78xkZJCA5mQ8s5Xn0um02iGzxcin1ORva4saz4IZ5DBFn/6fCDaQgSy9EWsi7ZXT9JAJWOHi0MC1QUJbF4R5IoWwd2KeYkbi9xh+MUx5rABHQfZLVTa4Fv7NdwQqP5Rc/B5H7cg8MTP9dxqJ53jdzh9OMuPH8YCQGZtxPG6V4iaBOcky7kfcTszc8T3xb57ntTED6vtn5W7iRv8X7g36/x0jcUKpSyBr8hMTf8zJ+TaTOobHP2GmeMjFZlx8rHi/27Qrhsb/yPdYY33Rh8f3fdn9F33u/67rC4zEkIIz7QpaqZAoNkaScUD55Gbp0kCjzfCv+Px/XcGTHstqlpgAAAAASUVORK5CYII=)
..........(3)
6. Information Gain of parameter Xi is computed using Equation.
Gain (Xi, T) = Info (T) - Info (Xi, T)..........(4)
7. Repeat the procedure 2 to 6 for all the parameters to get the vector.
G = [Gain (X1, T), Gain (X2, T), ..., Gain (Xn, T)]..........(5)
8. Choose the parameter X
i such that Gain (X
i, T) is higher than the other parameters considered. Identify the sub-branches under that node as below, if the probability distribution of the parameter X
i i.e. P is (for first iteration it will be root node) refer step 2.
p = (
p1, p2, p3, …, pn)
Then,
p1 = Subset of the dataset belongs to sub-branch 1.
p2 = Subset of the dataset belongs to sub-branch 2.
-
-
pn = Subset of the dataset belongs to sub-branch n.
9. Choose one of the sub-branch and respective subset of the dataset. Repeat the procedure 1-9 till last sub-branch will be the class node/leaf node.
10. Prune the tree if required (Logically not graphically).
11. Once the logical tree is ready verify this for the testing dataset.
Error rate allowed in output: +1 level or -1 level.
If error generated > ±1 then, repeat the procedure 1 to 11 for the sub-branch which is identified as misclassified.
II. Model is trained using training dataset and verified using testing dataset.
Now compare the suitability outcome of the decision tree approach with raster scan system.
a. If it is giving correct output with allowed error rate then display current suitability and expected improvements for potential suitability.
b. Else generate the outcome suitability using raster scan system and display it. In this case the conclusion is dataset selected is not appropriate, so we need to train the whole model again with another appropriate dataset.