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Open AccessJournal ArticleDOI

Analyzing trend and forecasting of rainfall changes in India using non-parametrical and machine learning approaches.

TLDR
The results derived from ECMWF ERA5 reanalysis data exhibit that increasing/decreasing precipitation convective rate, elevated low cloud cover and inadequate vertically integrated moisture divergence might have influenced on change of rainfall in India.
Abstract
This study analyzes and forecasts the long-term Spatio-temporal changes in rainfall using the data from 1901 to 2015 across India at meteorological divisional level. The Pettitt test was employed to detect the abrupt change point in time frame, while the Mann-Kendall (MK) test and Sen's Innovative trend analysis were performed to analyze the rainfall trend. The Artificial Neural Network-Multilayer Perceptron (ANN-MLP) was employed to forecast the upcoming 15 years rainfall across India. We mapped the rainfall trend pattern for whole country by using the geo-statistical technique like Kriging in ArcGIS environment. Results show that the most of the meteorological divisions exhibited significant negative trend of rainfall in annual and seasonal scales, except seven divisions during. Out of 17 divisions, 11 divisions recorded noteworthy rainfall declining trend for the monsoon season at 0.05% significance level, while the insignificant negative trend of rainfall was detected for the winter and pre-monsoon seasons. Furthermore, the significant negative trend (-8.5) was recorded for overall annual rainfall. Based on the findings of change detection, the most probable year of change detection was occurred primarily after 1960 for most of the meteorological stations. The increasing rainfall trend had observed during the period 1901-1950, while a significant decline rainfall was detected after 1951. The rainfall forecast for upcoming 15 years for all the meteorological divisions' also exhibit a significant decline in the rainfall. The results derived from ECMWF ERA5 reanalysis data exhibit that increasing/decreasing precipitation convective rate, elevated low cloud cover and inadequate vertically integrated moisture divergence might have influenced on change of rainfall in India. Findings of the study have some implications in water resources management considering the limited availability of water resources and increase in the future water demand.

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Journal ArticleDOI

Analysing the trend of rainfall in Asir region of Saudi Arabia using the family of Mann-Kendall tests, innovative trend analysis, and detrended fluctuation analysis

TL;DR: In this paper, the authors designed a study to analyse the annual rainfall variability and trend in 30 meteorological stations of the Asir region for the period of 1970-2017 using the Mann-Kendall (MK) test.
Journal ArticleDOI

Spatiotemporal trends in reference evapotranspiration and its driving factors in Bangladesh

TL;DR: In this article, the authors investigated spatiotemporal variations in ETo and the controlling factor of those variations using the modified Mann-Kendall test, empirical Bayesian kriging model, Morlet wavelet analysis (MWA), and cross-wavelet transform (XWT) model relying on daily climate data sets obtained from 18 meteorological stations for the period 1980-2017.
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Performance Comparison of an LSTM-based Deep Learning Model versus Conventional Machine Learning Algorithms for Streamflow Forecasting

TL;DR: In this paper, the authors compared the performance of four data-driven techniques of Linear Regression (LR), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) network in daily streamflow forecasting.
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CDLSTM: A Novel Model for Climate Change Forecasting

TL;DR: In this paper , a deep long short-term memory (DLSTM) model was developed and optimized to forecast all Himalayan states' temperature and rainfall values over the period of 1796-2013 and 1901-2015.
Journal ArticleDOI

The optimal alternative for quantifying reference evapotranspiration in climatic sub-regions of Bangladesh

TL;DR: The radiation-based model proposed by Abtew (ETo,6) was selected as an optimal alternative in all the sub-regions and whole Bangladesh against FAO56-PM model owing to its high accuracy, reliability in outlining substantial spatiotemporal variations of ETo, with very well linearly correlation with the FAO 56-PM and the least errors.
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