Error Estimation for Forecasting
of Orographic Rainfall Using Regression
Method
Pooja Verma, Swastika Chakraborty, and Pragya Jaiswal
Abstract Forecasting model of monthly orographic rainfall, which is fundamentally
characterized by a long occurrence period within a year not having much of maximum
rain rate values, has been developed using regression approach. The analysis has
been done on the basis of historical data of rainfall of two hill stations of different
altitude, Majitar and Ghum. Performance of the developed model is evaluated through
exhaustive error calculation. Goodness of fit value shows that the performance of the
developed model is acceptable for the two stations having different altitude. F-test
shows the statistical reliability of the prediction of rainfall. Lower root mean square
error (RMSE) value indicates good prediction of stochastic-deterministic events like
orographic rainfall.
Keywords Mean square error · Root mean square error · Mean absolute error ·
Orographic rainfall · Auto-regressive moving average · Auto-regressive integrated
moving average
1 Introduction
Rainfall Modeling
Orographic rainfall is caused by lifting of moist air across the upslope of hills. As
the air uplifts, it cools, resulting in orographic cloud which converts to widespread
long duration rainfall. Orographic rainfall over the hills covers nearly sixty five
percent of time during a year. As a consequence, instability of soil moisture causes
landslide affecting badly the habitat around the hills. Over the years, there are so many
attempts for the prediction of nonlinear time series [1] using regression approach
taking more than one input as a cause of rainfall and single input as a cause of
rainfall [2]. For the agricultural dependent economy like India, timely prediction of
P. Verma · S. Chakraborty (B) · P. Jaiswal
Electronics and Communication Engineering Department, Sikkim Manipal Institute of
Technology, Majitar, Rangpo, Sikkim, India
e-mail: swastika.c@smit.smu.edu.in
© Springer Nature Singapore Pte Ltd. 2021
C. Bhuiyan et al. (eds.), Water Security and Sustainability,
Lecture Notes in Civil Engineering 115,
https://doi.org/10.1007/978-981-15-9805-0_25
299
of Orographic Rainfall Using Regression
Method
Pooja Verma, Swastika Chakraborty, and Pragya Jaiswal
Abstract Forecasting model of monthly orographic rainfall, which is fundamentally
characterized by a long occurrence period within a year not having much of maximum
rain rate values, has been developed using regression approach. The analysis has
been done on the basis of historical data of rainfall of two hill stations of different
altitude, Majitar and Ghum. Performance of the developed model is evaluated through
exhaustive error calculation. Goodness of fit value shows that the performance of the
developed model is acceptable for the two stations having different altitude. F-test
shows the statistical reliability of the prediction of rainfall. Lower root mean square
error (RMSE) value indicates good prediction of stochastic-deterministic events like
orographic rainfall.
Keywords Mean square error · Root mean square error · Mean absolute error ·
Orographic rainfall · Auto-regressive moving average · Auto-regressive integrated
moving average
1 Introduction
Rainfall Modeling
Orographic rainfall is caused by lifting of moist air across the upslope of hills. As
the air uplifts, it cools, resulting in orographic cloud which converts to widespread
long duration rainfall. Orographic rainfall over the hills covers nearly sixty five
percent of time during a year. As a consequence, instability of soil moisture causes
landslide affecting badly the habitat around the hills. Over the years, there are so many
attempts for the prediction of nonlinear time series [1] using regression approach
taking more than one input as a cause of rainfall and single input as a cause of
rainfall [2]. For the agricultural dependent economy like India, timely prediction of
P. Verma · S. Chakraborty (B) · P. Jaiswal
Electronics and Communication Engineering Department, Sikkim Manipal Institute of
Technology, Majitar, Rangpo, Sikkim, India
e-mail: swastika.c@smit.smu.edu.in
© Springer Nature Singapore Pte Ltd. 2021
C. Bhuiyan et al. (eds.), Water Security and Sustainability,
Lecture Notes in Civil Engineering 115,
https://doi.org/10.1007/978-981-15-9805-0_25
299