Seasonal rainfall hindcasting using ensemble multi-stage genetic programming

Mehr, Ali Danandeh

doi:10.1007/s00704-020-03438-3

Cited by 26 publications

(6 citation statements)

References 40 publications

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“…Then, to improve the climate predictions, an ensemble method was employed. Recent studies have shown that ensemble averaged data generally offer better predictions than individual models do (Becker et al, 2014;Ma et al, 2015;Madadgar et al, 2016;Rahmani-Rezaeieh et al, 2020;Sajedi Hosseini et al, 2020;Sheikh Khozani et al, 2020;Danandeh Mehr, 2021). The employed technique combines multiple models to produce improved results, originating from the idea that when the dispersion of a model simulations increases, that model becomes more important (Shannon and Weaver, 1947;Hwang and Yoon, 1981).…”

Section: Methodsmentioning

confidence: 99%

Future Changes in Precipitation Over Northern Europe Based on a Multi-model Ensemble From CMIP6: Focus on Tana River Basin

Moradian

Haghighi

Asadi

et al. 2021

Preprint

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Accurate climate predictions help policymakers mitigate the negative effects of climate change and prioritize environmental issues based on scientific evidence. These predictions rely heavily on the outputs of GCMs (General Circulation Models), but the large number of GCMs and their different outputs in each region confuses researchers in their selection. In this paper, we analyzed the performance of a CMIP6 (Climate Model Intercomparison Project Phase 6) multi-model ensemble for precipitation data over Northern Europe. We first evaluated the overall performance of 12 CMIP6 models from GCMs in thirty years of 1985-2014. In addition, future projections were analyzed between 2071 and 2100 using SSP1-2.6 and SSP5-8.5 (Shared Socioeconomic Pathways). Then, simulations were statistically improved using an ensemble method to correct the systematic error of the CMIP6 models and then the capacity of postprocessed data to reproduce historical trends of climate events was investigated. Finally, the possible spatio-temporal changes of future precipitation data were explored in Tana River Basin. The results of this study show that different CMIP6 models do not have the same accuracy in estimating precipitation in the study area. However, the ensemble method can be effective in increasing the accuracy of the predictions. In addition, this study projected a change in the monthly precipitation data over Tana River Basin by 2.46% and 2.06% from 2071 to 2100 compared to the historical period, based on SSP1-2.6 and SSP5-8.5, respectively.

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Section: Methodsmentioning

confidence: 99%

Future Changes in Precipitation Over Northern Europe Based on a Multi-model Ensemble From CMIP6: Focus on Tana River Basin

Moradian

Haghighi

Asadi

et al. 2021

Preprint

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“…The model is developed on the train dataset and tested on the test dataset. The algorithms that are used for training are Logistic Regression [2], Decision Trees [13], KNN and Random Forest[14].…”

Section: Préprocessingmentioning

confidence: 99%

Short-term rainfall prédiction using prédictive analytics: A case study in Telangana

Gopala Krishna,

Rajashekhar

et al. 2024

E3S Web of Conf.

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Rainfall forecasting is critical because heavy rains can bring a full of tragedies. This prediction aids people in requiring preventive steps, and it should be correct. There are two types of rainfall forecasting: short-term forecasting and long-term forecasting. Predictions are typically correct for short-term, but creating a model for future rainfall prediction is the most difficult task. Because it is strongly tied to economy and human lives, heavy precipitation forecast could be a severe disadvantage for natural science departments. It’s the cause of natural disasters like floods and droughts that affect people all around the world every year. For countries like India, where agriculture is the primary source of income, the accuracy of rainfall estimates is critical. Regression may be used in the prediction of precipitation utilizing machine learning approaches. The goal of this work is to provide non-experts with easy access to the techniques and approaches used in the field of precipitation prediction, as well as a comparison of the various machine learning algorithms.

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“…Secondly, the WGP model is the best-performing model. Mehr (2021) collected rainfall data from the Muratpasa meteorology station located in Antalya downtown, Turkey to increase the accuracy of the seasonal rainfall forecasting. For this, they have created an ensemble evolutionary model that integrates two genetic programming techniques GEP & MSGP.…”

Section: Brief Literature Reviewmentioning

confidence: 99%

Study of the effectiveness of wavelet genetic programming model for water quality analysis in the Uttar Pradesh region

Saxena

Gaonkar

Singh

2023

Environ Monit Assess

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Water constitutes an essential part of the Earth as it helps in making the environment greener, supporting people to survive and in transporting various nutrients and minerals. But all these factors are drastically impacted due to rising water pollution and its poor sanitation leading to poor water quality. In India, according to the latest survey, due to the excessive use of chemicals by the industries, fertilizers and pesticides by the farmers, various water bodies are getting contaminated, causing a lack of good quality water suitable for consumption. About 2 lakh people die every year due to various diseases caused by consumption of contaminated water. Not only the surface water, groundwater and river water are also getting contaminated. Hence, there is a need to analyze the quality of water in the existing water sources. In this work, a work ow is proposed for analyzing the water quality in selected regions of Uttar Pradesh state. The proposed work uses a hybrid Wavelet Genetic Programming model for analysis and visualization of 13 rivers of Uttar Pradesh region.Prediction of Dissolved Oxygen (DO) level is used as the determinant for water quality assessment. Continuous Wavelet Transform is utilized to decompose the DO dataset and the decomposed DO values are treated as input variables for the model using Morlet wavelet function. The results have proved that the proposed model is suitable for the accurate prediction of DO values.

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Seasonal rainfall hindcasting using ensemble multi-stage genetic programming

Cited by 26 publications

References 40 publications

Future Changes in Precipitation Over Northern Europe Based on a Multi-model Ensemble From CMIP6: Focus on Tana River Basin

Future Changes in Precipitation Over Northern Europe Based on a Multi-model Ensemble From CMIP6: Focus on Tana River Basin

Short-term rainfall prédiction using prédictive analytics: A case study in Telangana

Study of the effectiveness of wavelet genetic programming model for water quality analysis in the Uttar Pradesh region

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