Assessment of five meteorological indices for monitoring the drought condition in Chengalpattu District, Tamilnadu, India

Kamalanandhini, M.; Annadurai, R.

doi:10.1016/j.matpr.2021.01.850

Cited by 6 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As per the recommendation of the Indian Meteorological Department (IMD), SPI is used in drought prediction in all the Indian states. There were studies on other indices, such as SPI, DI, EDI, PNI, and RAI [27]. Pandiyarajhan Govindasamy [28] compared the efficiency of SPI with the percent of normal precipitation index method used by IMD in drought forecasting.…”

Section: Droughtmentioning

confidence: 99%

A Synergistic Optimization Algorithm with Attribute and Instance Weighting Approach for Effective Drought Prediction in Tamil Nadu

Sundararajan,

Srinivasan

2024

Sustainability

View full text Add to dashboard Cite

The creation of frameworks for lowering natural hazards is a sustainable development goal specified by the United Nations. This study aims to predict drought occurrence in Tamil Nadu, India, using 26 years of data, with only 3 drought years. Since the drought-occurrence years are minimal, it is an imbalanced dataset, which gives a suboptimal classification performance. The accuracy metric has a tendency to produce misleadingly high results by focusing on the accuracy of forecasting the majority class while ignoring the minority class; hence, this work considers the metrics’ precision and recall. A novel strategy uses attribute (or instance) weighting, which allots weights to attributes (or instances) based on their importance, to improve precision and recall. These weights are found using a bio-inspired optimization algorithm, by designing its fitness function to improve precision and recall of the minority (drought) class. Since increasing precision and recall is a tug-of-war, multi-objective optimization helps to identify optimal attribute (or instance) weight balancing precision and recall while maximizing both. The newly introduced Synergistic Optimization Algorithm (SOA) is utilized for multi-objective optimization in order to ascertain weights for attributes (or instances). In SOA, to solve multi-objective optimization, each objective’s population was generated using three distinct algorithms, namely, the Genetic, Firefly, and Particle Swarm Optimization (PSO) algorithms. The experimental results demonstrated that the prediction performance for the minority drought class was superior when utilizing instance (or attribute) weighting compared to the approach not employing attribute/instance weighting. The Gradient Boosting classifier with an attribute-weighted dataset achieved precision and recall values of 0.92 and 0.79, whereas, with instance weighting, the values were 0.9 and 0.76 for the drought class. The attribute weighting shows that in addition to the default drought indices SPI and SPEI, pollution factors and mean sea level rise are valuable indicators in drought prediction. From instance weighting, it is inferred that the instances of the months of March, April, July, and August contribute most to drought prediction.

show abstract

Section: Droughtmentioning

confidence: 99%

A Synergistic Optimization Algorithm with Attribute and Instance Weighting Approach for Effective Drought Prediction in Tamil Nadu

Sundararajan,

Srinivasan

2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…Ethiopia's economy, which is mainly dependent on natural rain-fed agriculture, where irrigation accounts for <1.1% of the total cultivated land (Bewket & Conway, 2007), is negatively impacted by rainfall fluctuations (Belay et al, 2019). For example, a 10% decrease in seasonal rainfall of Ethiopia, compared to the climatology, translates to a 4.4% decrease in crop production (Braun, 1991), and a significant decreasing rainfall amount may lead to meteorological drought (Kamalanandhini & Annadurai, 2021). Because Ethiopia has less climate change adaptation potential, without proper management options with the aid of meteorological forecasts, a significant decrease in rainfall amount during the main rainy season exacerbated by late onset and untimely cessation of rainfall and higher evapotranspiration and frost occurrence may cause both meteorological and agricultural drought as well as famine in Ethiopia.…”

Section: Rainfall In Ethiopiamentioning

confidence: 99%

Historical Trend Analysis and Future Projections of Rainfall in Amhara, Ethiopia

Mekoya,

Molla,

Workneh

et al. 2024

East Afr. j. for. agrofor.

View full text Add to dashboard Cite

North-western Ethiopia has the greatest potential for lowland bamboo forest resources. The bamboo forest surrounding the region is important for ecological and economic reasons. However, no specific studies have been conducted to determine the variations of biomass and carbon in lowland bamboo forests across different parts of bamboo culms in both plantations and natural forests. Therefore, the main goal of this study was to know the variation of the above-ground biomass and carbon storage of lowland bamboo (Oxytenathera abyssinica) in different vegetative parts of the culm in the Pawe district, in both natural and planted forests. A total of 108 bamboo culms were destructively sampled (54 from plantations and 54 from natural forests). The samples were then separated into three parts: the culm, the branch, and the leaf portion. Each part's total fresh weight and sample fresh weight were immediately determined. The samples were placed in a paper bag and transported to the laboratory for analysis of their dry weight. The data was analyzed using R software for descriptive statistics to examine biomass variations among vegetative parts and age classes of natural and plantation bamboo forests. Managed bamboo forests show a 17% increase in total biomass production compared to natural forests. The medium-aged bamboo group accumulated more above-ground biomass than the younger and older age groups in both forest types, while density increased with age

show abstract

Inter-comparison on the Suitability of Rain-Based Meteorological Drought in Johor River Basin, Malaysia

2022

View full text Add to dashboard Cite

Assessment of five meteorological indices for monitoring the drought condition in Chengalpattu District, Tamilnadu, India

Cited by 6 publications

References 6 publications

A Synergistic Optimization Algorithm with Attribute and Instance Weighting Approach for Effective Drought Prediction in Tamil Nadu

A Synergistic Optimization Algorithm with Attribute and Instance Weighting Approach for Effective Drought Prediction in Tamil Nadu

Historical Trend Analysis and Future Projections of Rainfall in Amhara, Ethiopia

Inter-comparison on the Suitability of Rain-Based Meteorological Drought in Johor River Basin, Malaysia

Contact Info

Product

Resources

About