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137 results on '"Moharir, Kanak N."'

6. Conclusions

Author

Pande, Chaitanya B., Moharir, Kanak N., Negm, Abdelazim, Negm, Abdelazim M., Series Editor, Chaplina, Tatiana, Series Editor, Pande, Chaitanya B., editor, Moharir, Kanak N., editor, and Negm, Abdelazim, editor

Published
2023
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32. Forecasting of SPI and Meteorological Drought Based on the Artificial Neural Network and M5P Model Tree

Author

Pande, Chaitanya B., Al-Ansari, Nadhir, Kushwaha, N. L., Srivastava, Aman, Noor, Rabeea, Kumar, Manish, Moharir, Kanak N., and Elbeltagi, Ahmed

Subjects
drought forecasting, machine learning, Datavetenskap (datalogi), Computer Sciences, standard precipitation index, Water Engineering, Vattenteknik
Abstract

Climate change has caused droughts to increase in frequency and severity worldwide, which has attracted scientists to create drought prediction models to mitigate the impacts of droughts. One of the most important challenges in addressing droughts is developing accurate models to predict their discrete characteristics, i.e., occurrence, duration, and severity. The current research examined the performance of several different machine learning models, including Artificial Neural Network (ANN) and M5P Tree in forecasting the most widely used drought measure, the Standardized Precipitation Index (SPI), at both discrete time scales (SPI 3, SPI 6). The drought model was developed utilizing rainfall data from two stations in India (i.e., Angangaon and Dahalewadi) for 2000–2019, wherein the first 14 years are employed for model training, while the remaining six years are employed for model validation. The subset regression analysis was performed on 12 different input combinations to choose the best input combination for SPI 3 and SPI 6. The sensitivity analysis was carried out on the given best input combination to find the most effective parameter for forecasting. The performance of all the developed models for ANN (4, 5), ANN (5, 6), ANN (6, 7), and M5P models was assessed through the different statistical indicators, namely, MAE, RMSE, RAE, RRSE, and r. The results revealed that SPI (t-1) is the most sensitive parameters with highest values of β = 0.916, 1.017, respectively, for SPI-3 and SPI-6 prediction at both stations on the best input combinations i.e., combination 7 (SPI-1/SPI-3/SPI-4/SPI-5/SPI-8/SPI-9/SPI-11) and combination 4 (SPI-1/SPI-2/SPI-6/SPI-7) based on the higher values of R2 and Adjusted R2 while the lowest values of MSE values. It is clear from the performance of models that the M5P model has higher r values and lesser RMSE values as compared to ANN (4, 5), ANN (5, 6), and ANN (6, 7) models. Therefore, the M5P model was superior to other developed models at both stations. Validerad;2022;Nivå 2;2022-11-15 (hanlid)

Published
2022

33. Review of GIS Multi-Criteria Decision Analysis for Managed Aquifer Recharge in Semi-Arid Regions

Author

Fathi, Sajad, Sjåstad Hagen, Jenny, Matano, Alessia, E. H. Nogueira, Guilherme, Pande, Chaitanya B., Moharir, Kanak N., Water and Climate Risk, Pande, Chaitanya B., and Moharir, Kanak N.

Subjects
Flexibility (engineering), geography, Geographic information system, geography.geographical_feature_category, business.industry, Environmental resource management, Climate change, Aquifer, Groundwater recharge, Multiple-criteria decision analysis, Water scarcity, Environmental science, business, SDG 6 - Clean Water and Sanitation, Decision analysis
Abstract

Managed aquifer recharge (MAR) embeds techniques for purposefully increasing the recharge to confined or unconfined aquifers. For aquifers subject to freshwater extraction, MAR constitutes a central component for sustainability—particularly in semi-arid regions, where climate change and population growth drive water scarcity toward a complex societal problem. Geographic information systems (GIS) coupled with multi-criteria decision analysis (MCDA) can be used for MAR suitability mapping to identify suitable areas for implementation of MAR. In other words, GIS-MCDA aids decision-makers to effectively choose the appropriate locations, with the flexibility of incorporating local contingencies if needed. Although numerous applications of GIS-MCDA are reported in the literature, the important linkage between MAR technique selection and suitability mapping with GIS-MCDA is largely left unfocused. This chapter provides an updated overview of GIS-MCDA for MAR suitability mapping in semi-arid regions and exemplifies key features that depend on the MAR technique considered with subsequent influence on the methodology. The potential of suitability mapping is discussed along with shortcomings of this methodology. Lastly, directions for future research are hypothesized.

Published
2021

40. An integrated approach to delineate the groundwater potential zones in Devdari watershed area of Akola district, Maharashtra, Central India.

Author

Pande, Chaitanya B., Moharir, Kanak N., Singh, Sudhir Kumar, and Varade, Abhay M.

Abstract

The present research work accentuates the hydrogeological evaluation for Devdari watershed of Maharashtra, Central India, using remote sensing, GIS, and multi influencing factor (MIF). The thematic layers of land use/land cover, groundwater depth, slope, drainage, flow direction, flow accumulation and geomorphology were prepared using IRS-LISS-III satellite data coupled with Shuttle Radar Topography Mission data on 23.50 and 30 m spatial resolution, respectively. The raster layers of these themes were estimated by employing ArcGIS software 10.3 and multi-influencing factor method and subsequently assigned with the relative weights as per their groundwater potential characteristics. For acknowledgement of the groundwater potential map, all the thematic layers and their respective weightage values were subjected to weighted overlay analysis (WOA) method. The evolved map demonstrates six groundwater potential zones in the study area, i.e., poor to nil (0.27 km2), very poor (0.95 km2), poor (17.67 km2), moderate (0.33 km2), good (9.51 km2), very good (14.665 km2) and excellent (1.84 km2). A field check survey was carried out to ascertain the validity of groundwater potential maps. The results of study area disclose that the area of watershed with poor, moderate and good-to-excellent groundwater potential zones is evaluated to allow precipitated water to infiltrate into subsurface and finally contribute to groundwater recharge in the watershed area. [ABSTRACT FROM AUTHOR]

Published
2020
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46. Potential Impact of Changing Climate on the Sustainability of Potato (Solanum Tuberosum L.) Production in India

Author

Shah, Maksud Hasan, Ajaharuddin, Sk. Md., Kundu, Santanu, Kumawat, S. M., Atta, Kousik, Hossain, Akbar, Hembram, Anjan, Pramanik, Kalipada, Ali, Md. Sharafat, Islam, Saidul, Pande, Chaitanya B., Negm, Abdelazim M., Series Editor, Chaplina, Tatiana, Series Editor, Pande, Chaitanya B., editor, Moharir, Kanak N., editor, and Negm, Abdelazim, editor

Published
2023
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