Climatology at any point : A neural network solution

Abstract

lHkh mi;ksxdrkZvksa] ;kstuk cukus okyksa] vkink izca/ku dkfeZdksa] i;ZVu vkfn }kjk rkieku] vf/kdre rkieku] U;wure rkieku] ok;qeaMyh; nkc] o"kkZ vkfn tSls ekSle izkpyksa dh tyok;q foKku ij lwpukvksa dh mUur tkudkjh dh vR;kf/kd ek¡x jgh gSA fdlh LFkku fo'ks"k esa os/k’kkyk ds vHkko vkSj dHkh&dHkh nh?kZ vof/k ds igys ds vk¡dM+ksa dh vuqiyC/krk ds dkj.k ekSle foKku leqnk; ml LFkku fo’ks"k ds fy, visf{kr lwpukvksa dks miyC/k ugha djk ikrk gSA bl 'kks/k i= esa LFkkfud varosZ’ku ds {ks= esa U;wjy usVodZ ds rqyukRed u, vuqiz;ksx crk, x, gSaA iwjs ckjg eghuksa ds vf/kdre vkSj U;wure nksuksa rkiekuksa  ds fy, U;wjy usVodZ varosZ’ku fun’kZ fodflr fd, x, gSaA ;g ekWMy ml LFkku fo’ks"k ij lkekU; vf/kdre vkSj U;wure rkiekuksa dks rS;kj djus ds fy, lwpukvksa ds :i  esa v{kka’k] ns’kkUrj vkSj mUu;u tSlh HkkSxksfyd lwpukvksa dk mi;ksx djrk gSA varosZ’ku ds fy, LFkkfud ekWMyksa ds fu"iknuksa dh rqyuk vU; lkekU;r% iz;qDr i)fr ds lkFk dh xbZ gSA

Advance knowledge of information on  climatology of meteorological parameters like temperature, maximum temperature, minimum temperature, atmospheric pressure, rainfall etc are of great demands from all the users, planners, disaster managements personals, tourism etc. The information is required at the place concerned but this cannot be fulfilled by the meteorological community due to absent of observatory at that place and also some time absent of past data of long period. The present paper has described a comparatively new application of the neural network in the field of spatial interpolation. Neural network interpolation models are developed for both maximum and minimum temperatures for all the twelve months. The model utilizes geographical information like latitude, longitude and elevation as inputs to generate normal maximum and minimum temperatures at a place. The performances of the models are compared with the other commonly used method for spatial interpolation.